diff --git a/msx_assets/object/004_sugar_box/textured.mtl b/msx_assets/object/004_sugar_box/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/004_sugar_box/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/006_mustard_bottle/material_0.mtl b/msx_assets/object/006_mustard_bottle/material_0.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..e2fe8df6fc7c18740ea30a0035d82f2fa0413da2
--- /dev/null
+++ b/msx_assets/object/006_mustard_bottle/material_0.mtl
@@ -0,0 +1,7 @@
+# https://github.com/mikedh/trimesh
+newmtl material_0
+Ka 0.40000000 0.40000000 0.40000000
+Kd 0.40000000 0.40000000 0.40000000
+Ks 0.40000000 0.40000000 0.40000000
+Ns 1.00000000
+map_Kd material_0.png
\ No newline at end of file
diff --git a/msx_assets/object/006_mustard_bottle/textured.mtl b/msx_assets/object/006_mustard_bottle/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/006_mustard_bottle/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/015_peach/collision.obj b/msx_assets/object/015_peach/collision.obj
new file mode 100644
index 0000000000000000000000000000000000000000..61bedabd6dbe14fb7dc6f6ec4fe1fe22a9e8ae10
--- /dev/null
+++ b/msx_assets/object/015_peach/collision.obj
@@ -0,0 +1,9559 @@
+# https://github.com/mikedh/trimesh
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\ No newline at end of file
diff --git a/msx_assets/object/015_peach/material_0.mtl b/msx_assets/object/015_peach/material_0.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..e2fe8df6fc7c18740ea30a0035d82f2fa0413da2
--- /dev/null
+++ b/msx_assets/object/015_peach/material_0.mtl
@@ -0,0 +1,7 @@
+# https://github.com/mikedh/trimesh
+newmtl material_0
+Ka 0.40000000 0.40000000 0.40000000
+Kd 0.40000000 0.40000000 0.40000000
+Ks 0.40000000 0.40000000 0.40000000
+Ns 1.00000000
+map_Kd material_0.png
\ No newline at end of file
diff --git a/msx_assets/object/015_peach/textured.mtl b/msx_assets/object/015_peach/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/015_peach/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/018_plum/collision.obj b/msx_assets/object/018_plum/collision.obj
new file mode 100644
index 0000000000000000000000000000000000000000..bedc174a642b9c6bd5eae521d4c027cae81a9f63
--- /dev/null
+++ b/msx_assets/object/018_plum/collision.obj
@@ -0,0 +1,7927 @@
+# https://github.com/mikedh/trimesh
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diff --git a/msx_assets/object/018_plum/textured.mtl b/msx_assets/object/018_plum/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/018_plum/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/018_plum/textured.obj b/msx_assets/object/018_plum/textured.obj
new file mode 100644
index 0000000000000000000000000000000000000000..f46b2d6dfc7166003ce651e82452c178f3877847
--- /dev/null
+++ b/msx_assets/object/018_plum/textured.obj
@@ -0,0 +1,33149 @@
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diff --git a/msx_assets/object/021_bleach_cleanser/collision.obj b/msx_assets/object/021_bleach_cleanser/collision.obj
new file mode 100644
index 0000000000000000000000000000000000000000..8a7bdf9aef970e82fe9cbcdd431747ac5215dd49
--- /dev/null
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diff --git a/msx_assets/object/028_skillet_lid/textured.mtl b/msx_assets/object/028_skillet_lid/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/028_skillet_lid/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/054_softball/textured.obj b/msx_assets/object/054_softball/textured.obj
new file mode 100644
index 0000000000000000000000000000000000000000..27bd7657de30aef6a2c6b46971a98f495f585f3d
--- /dev/null
+++ b/msx_assets/object/054_softball/textured.obj
@@ -0,0 +1,33103 @@
+# https://github.com/mikedh/trimesh
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diff --git a/msx_assets/object/057_racquetball/collision.obj b/msx_assets/object/057_racquetball/collision.obj
new file mode 100644
index 0000000000000000000000000000000000000000..5c6f60b60404170b673e9baef0811ae2f7834450
--- /dev/null
+++ b/msx_assets/object/057_racquetball/collision.obj
@@ -0,0 +1,8034 @@
+# https://github.com/mikedh/trimesh
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\ No newline at end of file
diff --git a/msx_assets/object/065-e_cups/textured.mtl b/msx_assets/object/065-e_cups/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/065-e_cups/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/065-e_cups/textured.obj b/msx_assets/object/065-e_cups/textured.obj
new file mode 100644
index 0000000000000000000000000000000000000000..f48b1cbab97275f718e223b659f328545c0ea66a
--- /dev/null
+++ b/msx_assets/object/065-e_cups/textured.obj
@@ -0,0 +1,33383 @@
+# https://github.com/mikedh/trimesh
+mtllib material_0.mtl
+usemtl material_0
+v 0.00702550 -0.01543190 -0.02945102
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diff --git a/msx_assets/object/065-i_cups/material_0.mtl b/msx_assets/object/065-i_cups/material_0.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..e2fe8df6fc7c18740ea30a0035d82f2fa0413da2
--- /dev/null
+++ b/msx_assets/object/065-i_cups/material_0.mtl
@@ -0,0 +1,7 @@
+# https://github.com/mikedh/trimesh
+newmtl material_0
+Ka 0.40000000 0.40000000 0.40000000
+Kd 0.40000000 0.40000000 0.40000000
+Ks 0.40000000 0.40000000 0.40000000
+Ns 1.00000000
+map_Kd material_0.png
\ No newline at end of file
diff --git a/msx_assets/object/065-i_cups/textured.obj b/msx_assets/object/065-i_cups/textured.obj
new file mode 100644
index 0000000000000000000000000000000000000000..ae8122567ec2c294e2c0aa38794ee43583d0ce39
--- /dev/null
+++ b/msx_assets/object/065-i_cups/textured.obj
@@ -0,0 +1,33165 @@
+# https://github.com/mikedh/trimesh
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\ No newline at end of file
diff --git a/msx_assets/object/071_nine_hole_peg_test/material_0.mtl b/msx_assets/object/071_nine_hole_peg_test/material_0.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..e2fe8df6fc7c18740ea30a0035d82f2fa0413da2
--- /dev/null
+++ b/msx_assets/object/071_nine_hole_peg_test/material_0.mtl
@@ -0,0 +1,7 @@
+# https://github.com/mikedh/trimesh
+newmtl material_0
+Ka 0.40000000 0.40000000 0.40000000
+Kd 0.40000000 0.40000000 0.40000000
+Ks 0.40000000 0.40000000 0.40000000
+Ns 1.00000000
+map_Kd material_0.png
\ No newline at end of file
diff --git a/msx_assets/object/073-d_lego_duplo/textured.mtl b/msx_assets/object/073-d_lego_duplo/textured.mtl
new file mode 100644
index 0000000000000000000000000000000000000000..4505df836a59257fd6f42817679219afb9ee06ad
--- /dev/null
+++ b/msx_assets/object/073-d_lego_duplo/textured.mtl
@@ -0,0 +1,3 @@
+newmtl material_0
+# shader_type beckmann
+map_Kd texture_map.png
\ No newline at end of file
diff --git a/msx_assets/object/073-d_lego_duplo/textured.obj b/msx_assets/object/073-d_lego_duplo/textured.obj
new file mode 100644
index 0000000000000000000000000000000000000000..893a83f80dce1639a27629488b5c66e7b73dc202
--- /dev/null
+++ b/msx_assets/object/073-d_lego_duplo/textured.obj
@@ -0,0 +1,34047 @@
+# https://github.com/mikedh/trimesh
+mtllib material_0.mtl
+usemtl material_0
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+f 8821/8821 8786/8786 8830/8830
+f 8786/8786 8784/8784 8830/8830
+f 8784/8784 8783/8783 8830/8830
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+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/layers/__init__.py
@@ -0,0 +1,3 @@
+# from .equiv_layers import *
+from .layers import *
+from .layers_attention import *
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--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/layers/layers.py
@@ -0,0 +1,504 @@
+import math
+
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops.layers.torch import Rearrange
+
+
+## Fully connected Neural Network block - Multi Layer Perceptron
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim=16, n_layers=1, act='relu', batch_norm=True):
+        super(MLP, self).__init__()
+        activations = {'relu': nn.ReLU, 'sigmoid': nn.Sigmoid, 'tanh': nn.Tanh, 'leaky_relu': nn.LeakyReLU,
+                       'elu': nn.ELU, 'prelu': nn.PReLU, 'softplus': nn.Softplus, 'mish': nn.Mish,
+                       'identity': nn.Identity
+                       }
+
+        act_func = activations[act]
+        layers = [nn.Linear(in_dim, hidden_dim), act_func()]
+        for i in range(n_layers):
+            layers += [
+                nn.Linear(hidden_dim, hidden_dim),
+                nn.BatchNorm1d(hidden_dim) if batch_norm else nn.Identity(),
+                act_func(),
+            ]
+        layers.append(nn.Linear(hidden_dim, out_dim))
+
+        self._network = nn.Sequential(
+            *layers
+        )
+
+    def forward(self, x):
+        return self._network(x)
+
+# Identity layer
+class Identity(nn.Module):
+    def __init__(self, ):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return x
+
+
+# Resnet Blocks
+class ResnetBlockFC(nn.Module):
+    '''
+    Fully connected ResNet Block class.
+
+    Args:
+        size_in (int): input dimension
+        size_out (int): output dimension
+        size_h (int): hidden dimension
+    '''
+
+    def __init__(self, size_in, size_out=None, size_h=None):
+        super().__init__()
+        # Attributes
+        if size_out is None:
+            size_out = size_in
+
+        if size_h is None:
+            size_h = min(size_in, size_out)
+
+        self.size_in = size_in
+        self.size_h = size_h
+        self.size_out = size_out
+        # Submodules
+        self.fc_0 = nn.Linear(size_in, size_h)
+        self.fc_1 = nn.Linear(size_h, size_out)
+        self.actvn = nn.ReLU()
+
+        if size_in == size_out:
+            self.shortcut = None
+        else:
+            self.shortcut = nn.Linear(size_in, size_out, bias=False)
+        # Initialization
+        nn.init.zeros_(self.fc_1.weight)
+
+    def forward(self, x):
+        net = self.fc_0(self.actvn(x))
+        dx = self.fc_1(self.actvn(net))
+
+        if self.shortcut is not None:
+            x_s = self.shortcut(x)
+        else:
+            x_s = x
+
+        return x_s + dx
+
+
+class GaussianFourierProjection(nn.Module):
+    """Gaussian random features for encoding time steps."""
+
+    def __init__(self, embed_dim, scale=30.):
+        super().__init__()
+        # Randomly sample weights during initialization. These weights are fixed
+        # during optimization and are not trainable.
+        self.W = nn.Parameter(torch.randn(embed_dim // 2) * scale, requires_grad=False)
+
+    def forward(self, x):
+        x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
+        return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
+
+
+# https://gist.github.com/kevinzakka/dd9fa5177cda13593524f4d71eb38ad5
+class SpatialSoftArgmax(nn.Module):
+    """Spatial softmax as defined in [1].
+    Concretely, the spatial softmax of each feature
+    map is used to compute a weighted mean of the pixel
+    locations, effectively performing a soft arg-max
+    over the feature dimension.
+    References:
+        [1]: End-to-End Training of Deep Visuomotor Policies,
+        https://arxiv.org/abs/1504.00702
+    """
+
+    def __init__(self, normalize=False):
+        """Constructor.
+        Args:
+            normalize (bool): Whether to use normalized
+                image coordinates, i.e. coordinates in
+                the range `[-1, 1]`.
+        """
+        super().__init__()
+
+        self.normalize = normalize
+
+        self.temperatur = nn.Parameter(torch.ones(1), requires_grad=True)
+
+    def _coord_grid(self, h, w, device):
+        if self.normalize:
+            return torch.stack(
+                torch.meshgrid(
+                    torch.linspace(-1, 1, w, device=device),
+                    torch.linspace(-1, 1, h, device=device),
+                )
+            )
+        return torch.stack(
+            torch.meshgrid(
+                torch.arange(0, w, device=device),
+                torch.arange(0, h, device=device),
+            )
+        )
+
+    def forward(self, x):
+        assert x.ndim == 4, "Expecting a tensor of shape (B, C, H, W)."
+
+        # compute a spatial softmax over the input:
+        # given an input of shape (B, C, H, W),
+        # reshape it to (B*C, H*W) then apply
+        # the softmax operator over the last dimension
+        b, c, h, w = x.shape
+
+        # x = x * h * w
+        x = x * (h * w / self.temperatur)
+        # print(self.temperatur)
+        softmax = F.softmax(x.view(-1, h * w), dim=-1)
+
+        # create a meshgrid of pixel coordinates
+        # both in the x and y axes
+        xc, yc = self._coord_grid(h, w, x.device)
+
+        # element-wise multiply the x and y coordinates
+        # with the softmax, then sum over the h*w dimension
+        # this effectively computes the weighted mean of x
+        # and y locations
+        y_mean = (softmax * xc.flatten()).sum(dim=1, keepdims=True)
+        x_mean = (softmax * yc.flatten()).sum(dim=1, keepdims=True)
+
+        # concatenate and reshape the result
+        # to (B, C*2) where for every feature
+        # we have the expected x and y pixel
+        # locations
+        return torch.cat([x_mean, y_mean], dim=1).view(-1, c * 2)
+
+
+########################################################################################################################
+# Modules Temporal Unet
+########################################################################################################################
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, *args, **kwargs):
+        return self.fn(x, *args, **kwargs) + x
+
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.fn = fn
+        self.norm = LayerNorm(dim)
+
+    def forward(self, x):
+        x = self.norm(x)
+        return self.fn(x)
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim=1, unbiased=False, keepdim=True)
+        mean = torch.mean(x, dim=1, keepdim=True)
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv1d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = nn.Conv1d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        qkv = self.to_qkv(x).chunk(3, dim = 1)
+        q, k, v = map(lambda t: einops.rearrange(t, 'b (h c) d -> b h c d', h=self.heads), qkv)
+        q = q * self.scale
+
+        k = k.softmax(dim = -1)
+        context = torch.einsum('b h d n, b h e n -> b h d e', k, v)
+
+        out = torch.einsum('b h d e, b h d n -> b h e n', context, q)
+        out = einops.rearrange(out, 'b h c d -> b (h c) d')
+        return self.to_out(out)
+
+
+class TimeEncoder(nn.Module):
+    def __init__(self, dim, dim_out):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            SinusoidalPosEmb(dim),
+            nn.Linear(dim, dim * 4),
+            nn.Mish(),
+            nn.Linear(dim * 4, dim_out)
+        )
+
+    def forward(self, x):
+        return self.encoder(x)
+
+
+class SinusoidalPosEmb(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        device = x.device
+        half_dim = self.dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
+        emb = x[:, None] * emb[None, :]
+        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
+        return emb
+
+
+class Downsample1d(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = nn.Conv1d(dim, dim, kernel_size=3, stride=2, padding=1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Upsample1d(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv = nn.ConvTranspose1d(dim, dim, kernel_size=4, stride=2, padding=1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+class Conv1dBlock(nn.Module):
+    '''
+        Conv1d --> GroupNorm --> Mish
+    '''
+
+    def __init__(self, inp_channels, out_channels, kernel_size, padding=None, n_groups=8):
+        super().__init__()
+        self.block = nn.Sequential(
+            nn.Conv1d(inp_channels, out_channels, kernel_size, stride=1,
+                      padding=padding if padding is not None else kernel_size // 2),
+            Rearrange('batch channels n_support_points -> batch channels 1 n_support_points'),
+            nn.GroupNorm(n_groups, out_channels),
+            Rearrange('batch channels 1 n_support_points -> batch channels n_support_points'),
+            nn.Mish(),
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+
+class ResidualBlock(nn.Module):
+    ################
+    # Janner code
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1),
+            nn.BatchNorm2d(out_channels),
+            nn.ReLU())
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(out_channels))
+        self.downsample = downsample
+        self.relu = nn.ReLU()
+        self.out_channels = out_channels
+
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        if self.downsample:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+
+
+class ResidualTemporalBlock(nn.Module):
+
+    def __init__(self, inp_channels, out_channels, cond_embed_dim, n_support_points, kernel_size=5):
+        super().__init__()
+
+        self.blocks = nn.ModuleList([
+            Conv1dBlock(inp_channels, out_channels, kernel_size, n_groups=group_norm_n_groups(out_channels)),
+            Conv1dBlock(out_channels, out_channels, kernel_size, n_groups=group_norm_n_groups(out_channels)),
+        ])
+
+        # Without context conditioning, cond_mlp handles only time embeddings
+        self.cond_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(cond_embed_dim, out_channels),
+            Rearrange('batch t -> batch t 1'),
+        )
+
+        self.residual_conv = nn.Conv1d(inp_channels, out_channels, kernel_size=1, stride=1, padding=0) \
+            if inp_channels != out_channels else nn.Identity()
+
+    def forward(self, x, c):
+        '''
+            x : [ batch_size x inp_channels x n_support_points ]
+            c : [ batch_size x embed_dim ]
+            returns:
+            out : [ batch_size x out_channels x n_support_points ]
+        '''
+        h = self.blocks[0](x) + self.cond_mlp(c)
+        h = self.blocks[1](h)
+        res = self.residual_conv(x)
+        out = h + res
+
+        return out
+
+
+class TemporalBlockMLP(nn.Module):
+
+    def __init__(self, inp_channels, out_channels, cond_embed_dim):
+        super().__init__()
+
+        self.blocks = nn.ModuleList([
+            MLP(inp_channels, out_channels, hidden_dim=out_channels, n_layers=0, act='mish')
+        ])
+
+        # Without context conditioning, cond_mlp handles only time embeddings
+        self.cond_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(cond_embed_dim, out_channels),
+            # Rearrange('batch t -> batch t 1'),
+        )
+
+        self.last_act = nn.Mish()
+
+    def forward(self, x, c):
+        '''
+            x : [ batch_size x inp_channels x n_support_points ]
+            c : [ batch_size x embed_dim ]
+            returns:
+            out : [ batch_size x out_channels x n_support_points ]
+        '''
+        h = self.blocks[0](x) + self.cond_mlp(c)
+        out = self.last_act(h)
+        return out
+
+
+class FiLMResidualTemporalBlock(ResidualTemporalBlock):
+    def __init__(self, inp_channels, out_channels, cond_embed_dim, n_support_points, kernel_size=5):
+        super().__init__(inp_channels, out_channels, cond_embed_dim, n_support_points, kernel_size)
+        
+        # Replace the original cond_mlp with separate scale and bias MLPs
+        delattr(self, 'cond_mlp')  # Remove the original conditioning MLP
+        
+        self.scale_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(cond_embed_dim, out_channels),
+            Rearrange('batch t -> batch t 1'),
+        )
+        self.bias_mlp = nn.Sequential(
+            nn.Mish(),
+            nn.Linear(cond_embed_dim, out_channels),
+            Rearrange('batch t -> batch t 1'),
+        )
+
+    def forward(self, x, c):
+        '''
+            x : [ batch_size x inp_channels x n_support_points ]
+            c : [ batch_size x embed_dim ]
+            returns:
+            out : [ batch_size x out_channels x n_support_points ]
+        '''
+        # FiLM conditioning
+        scale = self.scale_mlp(c)
+        bias = self.bias_mlp(c)
+        h = self.blocks[0](x) * scale + bias
+        
+        h = self.blocks[1](h)
+        res = self.residual_conv(x)
+        out = h + res
+        return out
+
+
+def group_norm_n_groups(n_channels, target_n_groups=8):
+    if n_channels < target_n_groups:
+        return 1
+    for n_groups in range(target_n_groups, target_n_groups + 10):
+        if n_channels % n_groups == 0:
+            return n_groups
+    return 1
+
+
+def compute_padding_conv1d(L, KSZ, S, D, deconv=False):
+    '''
+    https://gist.github.com/AhmadMoussa/d32c41c11366440bc5eaf4efb48a2e73
+    :param L:       Input length (or width)
+    :param KSZ:     Kernel size (or width)
+    :param S:       Stride
+    :param D:       Dilation Factor
+    :param deconv:  True if ConvTranspose1d
+    :return:        Returns padding such that output width is exactly half of input width
+    '''
+    print(f"INPUT SIZE {L}")
+    if not deconv:
+        return math.ceil((S * (L / 2) - L + D * (KSZ - 1) - 1) / 2)
+    else:
+        print(L, S, D, KSZ)
+        pad = math.ceil(((L - 1) * S + D * (KSZ - 1) + 1 - L * 2) / 2)
+        print("PAD", pad)
+        output_size = (L - 1) * S - 2 * pad + D * (KSZ - 1) + 1
+        print("OUTPUT SIZE", output_size)
+        return pad
+
+
+def compute_output_length_maxpool1d(L, KSZ, S, D, P):
+    '''
+    https://pytorch.org/docs/stable/generated/torch.nn.MaxPool1d.html
+    :param L:       Input length (or width)
+    :param KSZ:     Kernel size (or width)
+    :param S:       Stride
+    :param D:       Dilation Factor
+    :param P:       Padding
+    '''
+    return math.floor((L + 2 * P - D * (KSZ - 1) - 1) / S + 1)
+
+
+if __name__ == "__main__":
+    b, c, h, w = 1, 64, 12, 12
+    x = torch.full((b, c, h, w), 0.00)
+
+    i_max = 4
+    true_max = torch.randint(0, 10, size=(b, c, 2))
+    for i in range(b):
+        for j in range(c):
+            x[i, j, true_max[i, j, 0], true_max[i, j, 1]] = 1000
+        # x[i, j, i_max, true_max] = 1
+    # x[0,0,0,0] = 1000
+    soft_max = SpatialSoftArgmax(normalize=True)(x)
+    soft_max2 = SpatialSoftArgmax(normalize=False)(x)
+    diff = soft_max - (soft_max2 / 12) * 2 - 1
+    resh = soft_max.reshape(b, c, 2)
+    assert torch.allclose(true_max.float(), resh)
+
+    exit()
+    test_scales = [1, 5, 10, 30, 50, 70, 100]
+    for scale in test_scales[::-1]:
+        i_max = 4
+        true_max = torch.randint(0, 10, size=(b, c, 2))
+        for i in range(b):
+            for j in range(c):
+                x[i, j, true_max[i, j, 0], true_max[i, j, 1]] = scale
+            # x[i, j, i_max, true_max] = 1
+        # x[0,0,0,0] = 1000
+        soft_max = SpatialSoftArgmax(normalize=False)(x)
+        resh = soft_max.reshape(b, c, 2)
+        assert torch.allclose(true_max.float(), resh), scale
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/layers/layers_attention.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/layers/layers_attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..a62734b8386c0e28906aacdac74494d71545c37e
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/layers/layers_attention.py
@@ -0,0 +1,194 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from inspect import isfunction
+from torch import einsum
+
+from cfdp.diffusion_policy.layers.layers import group_norm_n_groups
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return {el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            nn.GELU()
+        ) if not glu else GEGLU(dim, inner_dim)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    # return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+    return torch.nn.GroupNorm(num_groups=group_norm_n_groups(in_channels), num_channels=in_channels, eps=1e-6,
+                              affine=True)
+
+
+class CrossAttention(nn.Module):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, context=None, mask=None):
+        h = self.heads
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        attn = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', attn, v)
+        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
+        super().__init__()
+        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head,
+                                    dropout=dropout)  # is a self-attention
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
+                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        # Residual connections and layer normalization
+        x = self.attn1(self.norm1(x)) + x  # self attention
+        x = self.attn2(self.norm2(x), context=context) + x  # attention to context
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for trajectory-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to trajectory
+    """
+
+    def __init__(self, in_channels, n_heads, d_head,
+                 depth=1, dropout=0., context_dim=None):
+        super().__init__()
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+
+        # self.proj_in = nn.Conv2d(in_channels,
+        #                          inner_dim,
+        #                          kernel_size=1,
+        #                          stride=1,
+        #                          padding=0)
+        self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+
+        self.transformer_blocks = nn.ModuleList(
+            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
+             for d in range(depth)]
+        )
+
+        # self.proj_out = zero_module(nn.Conv2d(inner_dim,
+        #                                       in_channels,
+        #                                       kernel_size=1,
+        #                                       stride=1,
+        #                                       padding=0))
+        self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        b, c, h = x.shape
+        x_in = x
+        x = self.norm(x)
+        x = self.proj_in(x)
+        x = rearrange(x, 'b c h -> b h c')
+        for block in self.transformer_blocks:
+            x = block(x, context=context)
+        x = rearrange(x, 'b h c -> b c h', h=h)
+        x = self.proj_out(x)
+        return x + x_in
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cc3e1aae78facfc738728682da7dd111406fb6d
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/__init__.py
@@ -0,0 +1,5 @@
+from .diffusion_model_base import GaussianDiffusionModel, build_condition
+from .flow_matching_base import FlowMatchingModel
+from .mlp_model import MLPModel
+from .gaussian_diffusion_loss import FlowMatchingLoss, GaussianDiffusionLoss
+from .temporal_unet import TemporalUnet, ContextModel, PointUnet, UNET_DIM_MULTS
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/diffusion_model_base.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/diffusion_model_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..3be69a8ebbe0122570b68134115ea63767465354
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/diffusion_model_base.py
@@ -0,0 +1,689 @@
+"""
+Adapted from https://github.com/jannerm/diffuser
+"""
+import abc
+import time
+from collections import namedtuple
+from copy import copy
+
+import einops
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+import torch.nn as nn
+from abc import ABC
+import time
+from enum import Enum
+
+from torch.nn import DataParallel
+
+from cfdp.diffusion_policy.models.helpers import cosine_beta_schedule, Losses, exponential_beta_schedule
+from cfdp.diffusion_policy.models.sample_functions import extract, apply_hard_conditioning, guide_gradient_steps, \
+    ddpm_sample_fn, ddpm_sample_fn_stomp
+from cfdp.diffusion_policy.utils.utils import to_numpy
+from cfdp.utils.data_utils import interpolate_points, pad_tensor
+
+def make_timesteps(batch_size, i, device):
+    t = torch.full((batch_size,), i, device=device, dtype=torch.long)
+    return t
+
+
+def build_condition(model, dataset, input_dict):
+    # input_dict is already normalized
+    condition = None
+    if model.context_model is not None:
+        condition = dict()
+        task_normalized = input_dict[f'{dataset.data_key_task}_normalized']
+
+        # (normalized) features of variable environments
+        # if hasattr(dataset, 'variable_environment') and dataset.variable_environment:
+        #     env_normalized = input_dict[f'{dataset.field_key_env}_normalized']
+        #     condition['env'] = env_normalized
+        # obs_history
+        # breakpoint()
+        if hasattr(dataset, 'data_key_obs_history'): # TODO: Make it better...
+            obs_history = input_dict[f'{dataset.data_key_obs_history}_normalized']
+            # breakpoint()
+            condition['tasks'] = torch.cat((task_normalized, obs_history), dim = -1)
+            # breakpoint()
+        else:
+            condition['tasks'] = task_normalized
+    return condition
+
+class PredictionMode(Enum):
+    EPSILON = "epsilon"
+    V = "v"
+    X = "x"
+
+class GaussianDiffusionModel(nn.Module, ABC):
+
+    def __init__(self,
+                 model=None,
+                 variance_schedule='exponential',
+                 n_diffusion_steps=100,
+                 clip_denoised=True,
+                 prediction_mode="epsilon", # Accept string: "epsilon", "v", "x"
+                 use_snr_weight=False, #snr weight for training loss function
+                 loss_type='l2',
+                 context_model=None,
+                 device='cuda',
+                 **kwargs):
+        super().__init__()
+
+        self.model_name = 'DiffusionModel'
+
+        self.model = model
+
+        self.context_model = context_model
+
+        self.n_diffusion_steps = n_diffusion_steps
+
+        self.state_dim = self.model.state_dim
+
+        self.device = device
+        # debug information dispay
+        self.verbose = False
+        self.use_snr_weight = use_snr_weight
+
+        if variance_schedule == 'cosine':
+            betas = cosine_beta_schedule(n_diffusion_steps, s=0.008, a_min=0, a_max=0.999)
+        elif variance_schedule == 'exponential':
+            betas = exponential_beta_schedule(n_diffusion_steps, beta_start=1e-4, beta_end=1.0)
+        else:
+            raise NotImplementedError
+
+        alphas = 1. - betas
+        alphas_cumprod = torch.cumprod(alphas, axis=0)
+        alphas_cumprod_prev = torch.cat([torch.ones(1), alphas_cumprod[:-1]])
+
+        # print("betas shape: ", betas.shape)
+        # print("alpha shape: ", alphas.shape)
+        # print("alphas_cumprod_prev shape: ", alphas_cumprod_prev.shape)
+        # print("betas: ", betas)
+        # print("alphas:  ", alphas)
+        # print("alphas_cumprod_prev: ", alphas_cumprod_prev)
+       
+        self.clip_denoised = clip_denoised
+        # self.predict_epsilon = predict_epsilon
+        if isinstance(prediction_mode, str):
+            self.prediction_mode = PredictionMode(prediction_mode.lower())
+        elif isinstance(prediction_mode, PredictionMode):
+            self.prediction_mode = prediction_mode
+        else:
+            raise ValueError(f"Invalid prediction_mode: {prediction_mode}")
+
+        self.register_buffer('betas', betas)
+        self.register_buffer('alphas_cumprod', alphas_cumprod)
+        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))
+        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))
+        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))
+        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))
+        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)
+        self.register_buffer('posterior_variance', posterior_variance)
+
+        ## log calculation clipped because the posterior variance
+        ## is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped',
+                             torch.log(torch.clamp(posterior_variance, min=1e-20)))
+        self.register_buffer('posterior_mean_coef1',
+                             betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))
+        self.register_buffer('posterior_mean_coef2',
+                             (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))
+
+        ## get loss coefficients and initialize objective
+        self.loss_fn = Losses[loss_type]()
+
+        # Initialize list to store diffusion process data (for vissualization usage)
+        self.diffusion_history = []
+        self.grad_scaled_history = []
+    # ------------------------------------------ sampling ------------------------------------------#
+    def predict_noise_from_start(self, x_t, t, x0):
+        """
+        Model output can be 'epsilon', 'v', or 'x'
+        """
+        if self.prediction_mode == PredictionMode.EPSILON:
+            return x0
+        elif self.prediction_mode == PredictionMode.X:
+            return (
+                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - x0
+            ) / extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+        elif self.prediction_mode == PredictionMode.V:
+            # model predicts v; convert to epsilon:
+            # eps = sqrt(alpha_bar) * v + sqrt(1 - alpha_bar) * x_t
+            sqrt_ab = extract(self.sqrt_alphas_cumprod, t, x_t.shape)
+            sqrt_1mab = extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape)
+            v = x0
+            return sqrt_ab * v + sqrt_1mab * x_t
+        else:
+            raise ValueError(f"Unknown prediction_mode: {self.prediction_mode}")
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        '''
+        Model output can be 'epsilon', 'v', or 'x'
+        '''
+        if self.prediction_mode == PredictionMode.EPSILON:
+            return (
+                extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+            )
+        elif self.prediction_mode == PredictionMode.X:
+            return noise
+        elif self.prediction_mode == PredictionMode.V:
+            # model predicts v; convert to x0:
+            # x0 = sqrt(ᾱ)*x_t - sqrt(1-ᾱ)*v
+            sqrt_ab  = extract(self.sqrt_alphas_cumprod, t, x_t.shape)
+            sqrt_1mab = extract(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape)
+            v = noise
+            return sqrt_ab * x_t - sqrt_1mab * v
+        else:
+            raise ValueError(f"Unknown prediction_mode: {self.prediction_mode}")
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, hard_conds, context, t):
+        if context is not None:
+            context = self.context_model(context)
+
+        x_recon = self.predict_start_from_noise(x, t=t, noise=self.model(x, t, context['condition']))
+        # Store the current state, timestep, and reconstruction
+        # todo: hack here maybe, loop all x from -1 to 1
+        if self.clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        else:
+            assert RuntimeError()
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape,
+                    hard_conds, 
+                    context = None, 
+                    return_chain = False,
+                    sample_fn = ddpm_sample_fn,
+                    n_diffusion_steps_without_noise = 0,
+                    prior_trajectory = None,
+                    timestep = None,
+                    **sample_kwargs):
+        device = self.betas.device
+
+        batch_size = shape[0]
+        n_diffusion_steps = self.n_diffusion_steps # Number of diffusion timesteps.
+        # If given prior trajectory to replan, then use the timestep provided.
+        if prior_trajectory is not None:
+            # Expand prior trajectory to match batch size if needed
+            x = prior_trajectory.repeat(batch_size, 1, 1)
+            n_diffusion_steps = timestep
+        else:
+            x = torch.randn(shape, device=device)
+        
+        # print("In function p_sample_loop. Printing x.shape: ", x.shape)
+        # breakpoint()
+        x = apply_hard_conditioning(x, hard_conds)
+        chain = [x] if return_chain else None
+        for i in reversed(range(n_diffusion_steps_without_noise, n_diffusion_steps)):
+            t = make_timesteps(batch_size, i, device)
+            x, values = sample_fn(self, x, hard_conds, context, t, **sample_kwargs)
+            x = apply_hard_conditioning(x, hard_conds)
+            if return_chain:
+                chain.append(x)
+            
+        if return_chain:
+            chain = torch.stack(chain, dim=1)
+            return x, chain
+        print("x min:", x.min().item(), "x max:", x.max().item())
+        print("hard_conds: ", hard_conds)
+        return x
+
+    def eta_schedule(self, t_norm: torch.Tensor):
+        # t_norm in [0,1], 1=early, 0=late
+        # e.g., cosine ramp: more noise early, near-zero late
+        # return 0.5 * (1 - torch.cos(torch.pi * t_norm))  # in [0,1]
+        base = 0.5 * (1 - torch.cos(torch.pi * t_norm))
+        res = base**20.0
+        # if res < 0.2:
+        #     res = torch.tensor(0.2, device=t_norm.device)
+        return res
+
+    @torch.no_grad()
+    def _ddim_step_core(self, x_t, t, t_next, context, eta_t,
+                    x0_override_fn=None):
+        # model forward
+        model_out = self.model(x_t, t, context['condition'])
+        # 2) x0 from model_out
+        x0 = self.predict_start_from_noise(x_t, t=t, noise=model_out)
+        x0.clamp_(-1., 1.)
+        # 3) optional late-stage overwrite on x0
+        if x0_override_fn is not None:
+            x0_new = x0_override_fn(x0)  # may return x0 or blended x0
+            if x0_new is not None:
+                x0 = x0_new
+            #     x0 = apply_hard_conditioning(x0, hard_conds=None)  # or pass hard_conds via closure
+        # 4) compute epsilon consistent with (possibly) updated x0
+        #    default: from model_out (handles EPS/X/V)
+        pred_noise = self.predict_noise_from_start(x_t, t=t, x0=model_out)
+
+        if self.verbose:
+                print("noise diff:", (model_out - pred_noise).abs().mean().item())
+        # 5) last step or DDIM update
+        if (t_next < 0).all():
+            x_next = x0
+        else:
+            alpha      = extract(self.alphas_cumprod, t, x_t.shape)
+            alpha_next = extract(self.alphas_cumprod, t_next, x_t.shape)
+            sigma = (
+                eta_t * ((1 - alpha / alpha_next) * (1 - alpha_next) / (1 - alpha)).sqrt())
+            c = (1 - alpha_next - sigma**2).sqrt()
+            # breakpoint()
+            x_next = x0 * alpha_next.sqrt() + c * pred_noise
+            x_next = x_next + sigma * torch.randn_like(x_next)
+        return x_next, x0, model_out
+
+    @torch.no_grad()
+    def ddim_sample(
+        self, 
+        shape, 
+        hard_conds,
+        full_time_series=None,
+        start_timestep = None,
+        prior_trajectory = None,
+        context = None, 
+        return_chain = False,
+        # t_start_guide = torch.inf,
+        # guide = None,
+        # n_guide_steps = 1,
+        **sample_kwargs,
+    ):
+        # Adapted from https://github.com/ezhang7423/language-control-diffusion/blob/63cdafb63d166221549968c662562753f6ac5394/src/lcd/models/diffusion.py#L226
+        device = self.betas.device
+        batch_size = shape[0]
+
+        if full_time_series is None:
+            total_timesteps = self.n_diffusion_steps
+            sampling_timesteps = self.n_diffusion_steps // 4
+            full_time_series = torch.linspace(0, total_timesteps - 1, steps=sampling_timesteps + 1, device=device)
+            full_time_series = torch.cat((torch.tensor([-1], device=device), full_time_series))
+        
+        # sampling_timesteps = self.n_diffusion_steps // 4
+        # # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
+        if start_timestep is not None: # denoising from intermidiate timestep
+            times = full_time_series[full_time_series <= start_timestep].int().tolist()
+            times = list(reversed(times)) 
+            time_pairs = list(zip(times[:-1], times[1:]))
+        else:
+            times = list(reversed(full_time_series.int().tolist()))
+            time_pairs = list(zip(times[:-1], times[1:]))  # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
+
+        if self.verbose:
+            print("full time series: ", full_time_series)
+            print("time pairs: ", time_pairs)
+            print("whole time steps for DDIM sampling: ", len(time_pairs))
+        
+        if start_timestep is not None and prior_trajectory is not None:
+            x = prior_trajectory
+        else:
+            x = torch.randn(shape, device=device)
+            x = apply_hard_conditioning(x, hard_conds)
+
+        chain = [x] if return_chain else None
+        
+        if context is not None:
+            context = self.context_model(context)
+
+        for time, time_next in time_pairs:
+            t = make_timesteps(batch_size, time, device)
+            t_next = make_timesteps(batch_size, time_next, device)
+
+            # eta_t
+            t_norm = (time + 1e-8) / (total_timesteps - 1 + 1e-8)          # ~1 at start, 0 at end
+            eta_t = self.eta_schedule(torch.tensor(t_norm, device=device))
+            # eta_t = 0
+            x, x0, _ = self._ddim_step_core(
+                    x, t, t_next, context, eta_t,
+                    x0_override_fn=None
+            )
+
+            x = apply_hard_conditioning(x, hard_conds)
+
+            if return_chain: chain.append(x)
+            if (time_next < 0): break
+        
+        if return_chain:
+            chain = torch.stack(chain, dim=1)
+            return x, chain
+        return x
+    
+    @torch.no_grad()
+    def ddim_sample_with_guidance(
+        self,
+        shape,
+        hard_conds,
+        return_chain=False,
+        guide=None,
+        rank_fn=None,
+        start_timestep = None,
+        prior_trajectory = None,
+        context = None, 
+        sampling_timesteps=None,
+        **sample_kwargs,
+    ):
+        device = self.betas.device
+        batch_size = shape[0]
+        eta = 0
+        total_timesteps = self.n_diffusion_steps
+        sampling_timesteps = sampling_timesteps or total_timesteps // 4
+
+        if "t_start_guide" in sample_kwargs:
+            t_start_guide = sample_kwargs["t_start_guide"]
+            print("t_start_guide =", t_start_guide)
+        else:
+            print("No t_start_guide provided, t_start_guide = 3")
+            t_start_guide = 3
+
+        # ======== Construct Time Schedule ========
+        full_time_series = torch.linspace(0, total_timesteps - 1, 
+                                            steps=sampling_timesteps + 1, 
+                                            device=device)
+        full_time_series = torch.cat((torch.tensor([-1], device=device), full_time_series))
+        # middle_t = full_time_series[2].item()  # e.g., 2nd step from end
+        # middle_timesteps = make_timesteps(shape[0], middle_t, device)
+        # sampling_timesteps = self.n_diffusion_steps // 4
+        # # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
+        if start_timestep is not None: # denoising from intermidiate timestep
+            times = full_time_series[full_time_series <= start_timestep].int().tolist()
+            times = list(reversed(times)) 
+            time_pairs = list(zip(times[:-1], times[1:]))
+        else:
+            times = list(reversed(full_time_series.int().tolist()))
+            time_pairs = list(zip(times[:-1], times[1:]))  # [(T-1, T-2), (T-2, T-3), ..., (1, 0), (0, -1)]
+
+        # use guidance to overwrite x, or not apply guidance
+        def apply_rank_or_inpaint(x, t):
+            if rank_fn is None:
+                return x
+            # return rank_fn.rank_trajectory(x, traj_type="ee")
+            if t < 1:
+                if self.verbose:
+                    print("Applying rank function", type(rank_fn))
+                return rank_fn.rank_trajectory(x, traj_type="ee")
+            else:
+                if self.verbose:
+                    print("Applying inpaint function", type(rank_fn))
+                return rank_fn.inpaint_trajectory(x, traj_type="ee", num_windows=4, top_k=3)
+
+        def make_override_fn(step_or_t):
+            def _fn(x0):
+                # Add guidance gradients
+                x_over, _ = guide_gradient_steps(
+                    x0, hard_conds=hard_conds, guide=guide, **sample_kwargs)
+                # x_over = apply_hard_conditioning(x_over, hard_conds)
+                x_over = apply_rank_or_inpaint(x_over, step_or_t)
+                x_over = apply_hard_conditioning(x_over, hard_conds)
+                return x_over
+            return _fn
+
+        # ======== Initialize Trajectory ========
+        # x = torch.randn(shape, device=device) #or read from prior_trajectory
+        if start_timestep is not None and prior_trajectory is not None:
+            x = prior_trajectory
+        else:
+            x = torch.randn(shape, device=device)
+            x = apply_hard_conditioning(x, hard_conds)
+
+        # ======== Iterative Sampling + Guidance ========
+        if rank_fn is not None: rank_fn.reset()
+        chain = [x] if return_chain else None
+        
+        if context is not None:
+            context = self.context_model(context)
+
+        for time, time_next in time_pairs:
+            t = make_timesteps(batch_size, time, device)
+            t_next = make_timesteps(batch_size, time_next, device)
+
+            override_fn = make_override_fn(time) if (guide is not None and time <= t_start_guide) else None
+            # eta_t
+            t_norm = (time + 1e-8) / (total_timesteps - 1 + 1e-8)          # ~1 at start, 0 at end
+            eta_t = self.eta_schedule(torch.tensor(t_norm, device=device))
+            # eta_t = 0
+            x, x0, _ = self._ddim_step_core(
+                x, t, t_next, context, eta_t,
+                x0_override_fn=override_fn)
+
+            x = apply_hard_conditioning(x, hard_conds)
+
+            if return_chain: chain.append(x)
+            if (time_next < 0): break
+        
+        if return_chain:
+            chain = torch.stack(chain, dim=1)
+            return x, chain
+        return x
+
+    @torch.no_grad()
+    def conditional_sample(self, 
+                        hard_conds, 
+                        horizon = None, 
+                        batch_size = 1, 
+                        ddim = False, 
+                        prior_trajectory = None, 
+                        timestep = None, 
+                        **sample_kwargs):
+        '''
+            hard conditions : hard_conds : { (time, state), ... }
+        '''
+        horizon = horizon or self.horizon
+        shape = (batch_size, horizon, self.state_dim)
+
+
+        if self.verbose:
+            print("-------use DDIM------", ddim)
+        if ddim:
+            return self.ddim_sample_with_guidance(shape, hard_conds, sampling_timesteps=self.n_diffusion_steps, **sample_kwargs)
+            # return self.ddim_sample(shape, hard_conds, sampling_timesteps=self.n_diffusion_steps, **sample_kwargs)
+        return self.p_sample_loop(shape, 
+                                hard_conds, 
+                                prior_trajectory = prior_trajectory, 
+                                timestep = timestep, 
+                                **sample_kwargs)
+
+    def forward(self, cond, *args, **kwargs):
+        raise NotImplementedError
+        return self.conditional_sample(cond, *args, **kwargs)
+
+    @torch.no_grad()
+    def warmup(self, horizon=64, device='cuda'):
+        shape = (2, horizon, self.state_dim)
+        x = torch.randn(shape, device=device)
+        t = make_timesteps(2, 1, device)
+        self.model(x, t, context=None)
+
+    @torch.no_grad()
+    def run_inference(self, context=None, hard_conds=None, n_samples=1, return_chain=False, **diffusion_kwargs):
+        # clear diffusion history record
+        self.diffusion_history = []
+        self.grad_scaled_history = []
+
+        # context and hard_conds must be normalized
+        hard_conds = copy(hard_conds)
+        context = copy(context)
+
+        # repeat hard conditions and contexts for n_samples
+        for k, v in hard_conds.items():
+            new_state = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+            hard_conds[k] = new_state
+
+        if context is not None:
+            for k, v in context.items():
+                context[k] = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+
+        # Sample from diffusion model
+        samples, chain = self.conditional_sample(
+            hard_conds, context=context, batch_size=n_samples, return_chain=True, **diffusion_kwargs
+        )
+
+        # chain: [ n_samples x (n_diffusion_steps + 1) x horizon x (state_dim)]
+        # extract normalized trajectories
+        trajs_chain_normalized = chain
+
+        # trajs: [ (n_diffusion_steps + 1) x n_samples x horizon x state_dim ]
+        trajs_chain_normalized = einops.rearrange(trajs_chain_normalized, 'b diffsteps h d -> diffsteps b h d')
+
+        if return_chain:
+            return trajs_chain_normalized
+
+        # return the last denoising step
+        return trajs_chain_normalized[-1]
+
+    @torch.no_grad()
+    def run_inference_with_replanning(self, 
+                                    context = None, 
+                                    hard_conds = None,
+                                    n_samples = 1, 
+                                    return_chain = False, 
+                                    trajectory_prior = None, 
+                                    choice: str = None, 
+                                    trajectory_length: int = None, 
+                                    timestep: int = None, 
+                                    **diffusion_kwargs):
+        # clear diffusion history record
+        self.diffusion_history = []
+        self.grad_scaled_history = []
+        # Extract the trajectory to replan from the prior trajectory.
+        if len(trajectory_prior.shape) == 2:
+            trajectory_prior = trajectory_prior.unsqueeze(0)
+        trajectory_prior = trajectory_prior.to(self.device)
+        trajectory = trajectory_prior
+        if choice == 'pad':
+            # Pad the trajectory to the original length
+            trajectory = pad_tensor(trajectory.squeeze(0), trajectory_length, pad_front = False).unsqueeze(0)
+        elif choice == 'interpolate':
+            # Interpolate the trajectory to the original length
+            trajectory = interpolate_points(trajectory, trajectory_length, resample=True)
+        else:
+            raise ValueError(f"Invalid choice: {choice}")       
+        # print("After Interpolation/Padding Trajectory Shape: ", trajectory.shape)
+        # breakpoint()
+
+        # Add noise for replanning:
+        timesteps = torch.full((trajectory.shape[0],), timestep, device=trajectory.device) # Convert timestep to tensor and expand to match batch dimension
+        traj_noisy = self.q_sample(x_start=trajectory, t=timesteps, noise=torch.randn_like(trajectory))
+
+        # context and hard_conds must be normalized
+        hard_conds = copy(hard_conds)
+        context = copy(context)
+
+        # repeat hard conditions and contexts for n_samples
+        for k, v in hard_conds.items():
+            new_state = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+            hard_conds[k] = new_state
+
+        if context is not None:
+            for k, v in context.items():
+                context[k] = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+
+        # Sample from diffusion model
+        samples, chain = self.conditional_sample(
+            hard_conds, 
+            context = context, 
+            batch_size = n_samples, 
+            return_chain =True, 
+            prior_trajectory=traj_noisy, 
+            timestep=timestep, 
+            **diffusion_kwargs
+        )
+
+        # chain: [ n_samples x (n_diffusion_steps + 1) x horizon x (state_dim)]
+        # extract normalized trajectories
+        trajs_chain_normalized = chain
+
+        # trajs: [ (n_diffusion_steps + 1) x n_samples x horizon x state_dim ]
+        trajs_chain_normalized = einops.rearrange(trajs_chain_normalized, 'b diffsteps h d -> diffsteps b h d')
+
+        if return_chain:
+            return trajs_chain_normalized
+
+        # return the last denoising step
+        return trajs_chain_normalized[-1]
+    # ------------------------------------------ training ------------------------------------------#
+
+    def q_sample(self, x_start, t, noise=None):
+        if noise is None:
+            noise = torch.randn_like(x_start)
+
+        sample = (
+                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
+        )
+        return sample
+
+    def p_losses(self, x_start, context, t, hard_conds):
+        noise = torch.randn_like(x_start)
+
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        x_noisy = apply_hard_conditioning(x_noisy, hard_conds)
+
+        # context model
+        if context is not None:
+            context = self.context_model(context)
+
+        # diffusion model
+        x_recon = self.model(x_noisy, t, context['condition'])
+        x_recon = apply_hard_conditioning(x_recon, hard_conds)
+
+        assert noise.shape == x_recon.shape
+
+        if self.prediction_mode == PredictionMode.EPSILON:
+            loss, info = self.loss_fn(x_recon, noise)
+        elif self.prediction_mode == PredictionMode.X:
+            loss, info = self.loss_fn(x_recon, x_start)
+        elif self.prediction_mode == PredictionMode.V:
+            # model outputs v-hat; build v_target = sqrt(ab)*eps - sqrt(1-ab)*x0
+            sqrt_ab   = extract(self.sqrt_alphas_cumprod, t, x_start.shape) # sqrt(ᾱ_t)
+            sqrt_1mab = extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) # sqrt(1-ᾱ_t)
+            v_target = sqrt_ab * noise - sqrt_1mab * x_start  # v = sqrt(ab)*eps - sqrt(1-ab)*x0
+            
+            # Option A: plain loss via your loss_fn (assumed MSE)
+            if not self.use_snr_weight:
+                loss, info = self.loss_fn(x_recon, v_target)
+            # Option B: SNR-weighted MSE (recommended for v-pred)
+            # refer to paper: PROGRESSIVE DISTILLATION FOR FAST SAMPLING OF DIFFUSION MODELS
+            # reduce weight of high noise area
+            else:
+                # SNR_t = ᾱ_t / (1-ᾱ_t)
+                ab = sqrt_ab ** 2
+                snr = ab / (1.0 - ab).clamp(min=1e-8)
+                w = snr / (snr + 1.0)  # stable reweighting
+                # match dims for broadcasting (B -> Bx1x...):
+                while w.ndim < v_target.ndim:
+                    w = w.unsqueeze(-1)
+                mse = (x_recon - v_target).pow(2)
+                loss = (w * mse).mean()
+                info = {
+                    "snr_weighted": True,
+                    "snr_mean": snr.mean().item(),
+                    "mse_mean": mse.mean().item(),
+                }
+        else:
+            raise ValueError(f"Unknown prediction_mode: {self.prediction_mode}")
+
+        return loss, info
+
+    def loss(self, x, context, *args):
+        batch_size = x.shape[0]
+        t = torch.randint(0, self.n_diffusion_steps, (batch_size,), device=x.device).long()
+        return self.p_losses(x, context, t, *args)
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/flow_matching_base.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/flow_matching_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..b906aa76c28b68ef18dbb56b6ed786dfa538ae3c
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/flow_matching_base.py
@@ -0,0 +1,129 @@
+from copy import copy
+
+import einops
+import torch
+import torch.nn as nn
+from abc import ABC
+
+from cfdp.diffusion_policy.models.sample_functions import apply_hard_conditioning
+
+class FlowMatchingModel(nn.Module, ABC):
+    def __init__(self,
+                 model=None,
+                 nonuniform_time=False,  # Use sigmoid-transformed normal distribution for time sampling
+                 context_model=None,
+                 device='cuda',
+                 **kwargs):
+        super().__init__()
+        self.model_name = 'FlowMatchingModel'
+        self.model = model # Unet model
+        self.nonuniform_time = nonuniform_time
+        self.context_model = context_model
+        self.state_dim = self.model.state_dim
+        self.device = device
+
+    def forward(self, x, hard_conds, context):
+        # x, action 
+        # hard_conds, start & goal hard constraints
+        # context, task-specific conditioning, environment observation
+        b = x.size(0)
+        if self.nonuniform_time:
+            # Sample time values from sigmoid-transformed normal distribution
+            nt = torch.randn((b,)).to(x.device)
+            t = torch.sigmoid(nt)
+        else:
+            # Sample time values uniformly from [0,1]
+            t = torch.rand((b,)).to(x.device)
+        texp = t.view([b, *([1] * len(x.shape[1:]))])
+        z1 = torch.randn_like(x)
+        z1 = apply_hard_conditioning(z1, hard_conds)
+        zt = (1 - texp) * x + texp * z1
+        
+
+        if context is not None:
+            context = self.context_model(context)
+        # match input & outpu & conditions
+        vtheta = self.model(zt, t, context['condition'])
+
+        batchwise_mse = ((z1 - x - vtheta) ** 2).mean(dim=list(range(1, len(x.shape))))
+        tlist = batchwise_mse.detach().cpu().reshape(-1).tolist()
+        ttloss = [(tv, tloss) for tv, tloss in zip(t, tlist)]
+        return batchwise_mse.mean(), ttloss
+
+    @torch.no_grad()
+    def warmup(self, horizon=64, device='cuda'):
+        shape = (2, horizon, self.state_dim)
+        x = torch.randn(shape, device=device)
+        t = torch.full((2,), 1, device=device, dtype=torch.long)
+        self.model(x, t, context=None)
+
+    @torch.no_grad()
+    def run_inference(self, context=None, hard_conds=None, n_samples=1, return_chain=False, **inference_kwargs):
+        # hard_conds and context must be normalized
+        hard_conds = copy(hard_conds)
+        context = copy(context)
+        # repeat hard conditions and contexts for n_samples
+        for k, v in hard_conds.items():
+            new_state = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+            hard_conds[k] = new_state
+
+        if context is not None:
+            for k, v in context.items():
+                context[k] = einops.repeat(v.to(self.device), 'd -> b d', b=n_samples)
+        
+        # Sample from flow matching model
+        # TODO: sample z in x dimension [b, T, state_dim]
+        trajs_chain = self.sample(hard_conds = hard_conds, context = context, 
+                                  batch_size = n_samples, **inference_kwargs)
+        
+        # trajs_chain is a list of tensors, each with shape (b, T, state_dim)
+        if return_chain:
+            # Stack along first dimension to get (sample_steps+1, b, T, state_dim)
+            trajs_chain = torch.stack(trajs_chain, dim=0)
+            return trajs_chain
+        else:
+            return trajs_chain[-1]
+        
+        
+
+    @torch.no_grad()
+    def sample(self, hard_conds, horizon=None, context=None, 
+               batch_size=1, null_cond=None, sample_steps=5, cfg=2.0, guide=None, **inference_kwargs):
+        z = torch.randn(batch_size, horizon, self.state_dim).to(self.device)
+        b = batch_size
+        dt = 1.0 / sample_steps
+        dt = torch.tensor([dt] * b).to(z.device).view([b, *([1] * len(z.shape[1:]))])
+        trajs_chain = [z]
+        print("guide function is: ", guide)
+        # TODO: add guidance will totally ruin the flow
+        
+
+        for i in range(sample_steps, 0, -1): 
+            t = i / sample_steps
+            t = torch.tensor([t] * b).to(z.device)
+            
+            if context is not None:
+                conidtion = self.context_model(context)
+
+            vc = self.model(z, t, conidtion['condition'])
+
+            # if null_cond is not None:
+            #     vu = self.model(z, t, null_cond)
+            #     vc = vu + cfg * (vc - vu)
+
+            # if i<=3 and guide is not None:
+            #     vu = 200*guide(z)
+            #     vc_norm = torch.norm(vc)
+            #     vu_norm = torch.norm(vu)
+            #     norm_ratio = vc_norm / vu_norm
+            #     print(f"Norm ratio (vc/vu): {norm_ratio:.4f}")
+            #     vc = (1-0.05) * vc + 0.05 * vu
+            
+            z = z - dt * vc
+            z = apply_hard_conditioning(z, hard_conds)
+            trajs_chain.append(z)
+        
+        return trajs_chain
+        
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/gaussian_diffusion_loss.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/gaussian_diffusion_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e1ae3be77635ced02b5d384a1c336f8ded18854
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/gaussian_diffusion_loss.py
@@ -0,0 +1,52 @@
+import torch
+from cfdp.diffusion_policy.models import build_condition
+
+
+class GaussianDiffusionLoss:
+
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def loss_fn(diffusion_model, input_dict, dataset, step=None):
+        """
+        Loss function for training diffusion-based generative models.
+        """
+        # traj_normalized = input_dict[f'{dataset.field_key_traj}_normalized']
+
+        traj_normalized = input_dict[f'{dataset.data_key_traj}_normalized']
+
+        condition = build_condition(diffusion_model, dataset, input_dict)
+
+        hard_conds = input_dict.get('hard_conds', {})
+        # print("hard_conds: ", hard_conds)
+        # print("trajectory normalized shape: ", traj_normalized.shape)
+        # print("trajectory normalized start: ", traj_normalized[0])
+        loss, info = diffusion_model.loss(traj_normalized, condition, hard_conds)
+
+        loss_dict = {'diffusion_loss': loss}
+
+        return loss_dict, info
+
+
+class FlowMatchingLoss:
+    def __init__(self):
+        pass
+
+    @staticmethod
+    def loss_fn(model, input_dict, dataset, step=None):
+        """
+        Loss function for training diffusion-based generative models.
+        """
+        traj_normalized = input_dict[f'{dataset.data_key_traj}_normalized']
+
+        condition = build_condition(model, dataset, input_dict)
+
+        hard_conds = input_dict.get('hard_conds', {})
+ 
+        loss, _ = model(traj_normalized, hard_conds, condition)
+        # loss, info = diffusion_model.loss(traj_normalized, context, hard_conds)
+
+        loss_dict = {'diffusion_loss': loss}
+
+        return loss_dict, _
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/guide_managers.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/guide_managers.py
new file mode 100644
index 0000000000000000000000000000000000000000..c555d46825819ab96dd1c910cb963a86ff57eb00
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/guide_managers.py
@@ -0,0 +1,1636 @@
+import abc
+import time
+from dataclasses import dataclass, field
+
+from typing import Dict, List
+import einops
+import numpy as np
+import sapien
+import torch
+from enum import Enum
+import math
+from torch import nn
+import torch.nn.functional as F
+
+from cfdp.utils.pointcloud_sdf import PointCloud_CSDF
+from cfdp.utils.data_utils import transform_ortho6d_to_quat
+from cfdp.utils.advanced_kinematics_solver import AdvancedKinematicsSolver
+
+@dataclass
+class DebugEntry: #for sphere based guidance debug
+    sphere_poses: any = None
+    sphere_radii: any = None
+    sdf_costs: any = None
+    spheres_after_guidance: any = None
+    sampled_spheres: any = None
+
+class SampleHelper:
+    # can be expand to Gaussian / spline sampling
+    # define input and output
+    def __init__(self):
+        pass
+
+    def catmull_rom_spline(self, p0, p1, p2, p3, t):  
+        # p0...p3: (..., 7)
+        # t: [num_samples, 1]
+        # Returns: [num_samples, 7]
+        # TODO: batch fit & samples
+        a = 2*p1
+        b = -p0 + p2
+        c = 2*p0 - 5*p1 + 4*p2 - p3
+        d = -p0 + 3*p1 - 3*p2 + p3
+        return 0.5 * (a + b * t + c * t**2 + d * t**3)
+
+class GuideManager(nn.Module, abc.ABC):
+    def __init__(self,
+                 dataset,
+                 tensor_args,
+                 clip_grad=False, clip_grad_rule='norm',
+                 max_grad_norm=1.0, max_grad_value=1.0,
+                 guidance_weight=0.1,
+                 ):
+        super().__init__()
+        self.dataset = dataset
+        self.clip_grad = clip_grad
+        self.clip_grad_rule = clip_grad_rule
+        self.max_grad_norm = max_grad_norm
+        self.max_grad_value = max_grad_value
+        self.device = tensor_args['device']
+        self.dtype = tensor_args['dtype']
+        self.guidance_weight = guidance_weight
+
+    def forward(self, x_normalized):
+        grad = torch.full_like(x_normalized, fill_value=0.0)
+        return grad
+
+    def update_observation(self, obs):
+        pass
+
+    def clip_gradient(self, grad):
+        if self.clip_grad:
+            if self.clip_grad_rule == 'norm':
+                return self.clip_grad_by_norm(grad)
+            elif self.clip_grad_rule == 'value':
+                return self.clip_grad_by_value(grad)
+            else:
+                raise NotImplementedError
+        else:
+            return grad
+
+    def clip_grad_by_norm(self, grad):
+        # clip gradient by norm
+        if self.clip_grad:
+            grad_norm = torch.linalg.norm(grad + 1e-6, dim=-1, keepdims=True)
+            scale_ratio = torch.clip(grad_norm, 0., self.max_grad_norm) / grad_norm
+            grad = scale_ratio * grad
+        return grad
+
+    def clip_grad_by_value(self, grad):
+        # clip gradient by value
+        if self.clip_grad:
+            grad = torch.clip(grad, -self.max_grad_value, self.max_grad_value)
+        return grad
+    
+    def reset(self):
+        pass
+    
+def running_avg(tensor, window_size):
+    """
+    Computes running average using PyTorch's conv1d operation.
+    
+    :param tensor: Input tensor of shape [batch_size, seq_len, dim]
+    :param window_size: Size of the sliding window
+    :return: Tensor with same shape as input, containing running averages
+    """
+    # PyTorch doesn't have a direct convolve function like numpy
+    # Need to handle batch dimensions properly for trajectory data
+    batch_size, seq_len, dim = tensor.shape
+    result = torch.zeros_like(tensor)
+    # Create the kernel for the running average
+    kernel = torch.ones(window_size, device=tensor.device) / window_size
+    # Apply running average to each dimension separately
+    for b in range(batch_size):
+        for d in range(dim):
+            # Use 1D convolution for running average
+            # F.conv1d expects input shape [batch, channels, length]
+            padded = torch.nn.functional.pad(tensor[b, :, d].unsqueeze(0).unsqueeze(0), 
+                                            (window_size-1, 0), mode='reflect')
+            convolved = torch.nn.functional.conv1d(padded, 
+                                                  kernel.view(1, 1, -1))
+            result[b, :, d] = convolved.squeeze()
+    return result
+
+class GuideManagerPath(GuideManager):
+    def __init__(self,
+                 dataset,
+                 tensor_args,
+                 clip_grad=False, clip_grad_rule='norm',
+                 max_grad_norm=1.0, max_grad_value=1.0,
+                 guidance_weight=0.15,
+                 ):
+        super().__init__(dataset, 
+                         tensor_args,
+                         clip_grad, clip_grad_rule, 
+                         max_grad_norm, max_grad_value,
+                         guidance_weight)
+        self._point_cloud = None
+        self._sdf_model = PointCloud_CSDF(sphere_radius=0.10, max_distance=0.04, device=self.device, pcd=None)
+        # print("GUIDANCE STRENGTH: ", self.guidance_weight)
+
+    def forward(self, x_normalized): 
+        #calculate sdf guidance
+        x_norm = x_normalized.clone()
+        with torch.enable_grad():
+            x_norm.requires_grad_(True)
+            x = self.dataset.unnormalize_trajectory(x_norm)
+            # calculate sdf guidance according to x and environment
+            sdf_guidance = self._calculate_sdf_guidance(x)
+            # print("sdf_guidance: ", sdf_guidance)
+            smooth_guidance = self._calculate_smooth_guidance(x)
+        # print("sdf_guidance: ", sdf_guidance)
+        # guidance = 0.1*sdf_guidance + 0.01*smooth_guidance
+        guidance = 0.1*sdf_guidance
+        # guidance = self.clip_gradient(guidance)
+        return self.guidance_weight*guidance
+
+    # Update observation for calculation this guidance
+    # This function is required to be called before forward()
+    def update_observation(self, obs):
+        self._point_cloud = obs['point_cloud']
+        self._sdf_model.update_pcd(self._point_cloud)
+    
+    def _calculate_sdf_guidance(self, x_unnormalized):
+        # Extract position coordinates (first 3 dimensions) and detach to handle gradients manually
+        positions = x_unnormalized[..., :3].detach().requires_grad_(True)
+        sdf_values = self._sdf_model(positions)  # [batch, length]
+        # print("positions shape:", positions.shape)
+        # print(f'SDF values: {sdf_values.cpu().detach().numpy()}')
+        # Calculate gradients through backward pass
+        sdf_values.sum().backward()
+        sdf_gradient = positions.grad  # Get gradient from positions tensor instead
+        sdf_gradient[:, 0] = 0.0
+        sdf_gradient[:, -1] = 0.0
+        running_avg_sdf_gradient = running_avg(sdf_gradient, 3)
+        # print(f'SDF gradient: {sdf_gradient.cpu().detach().numpy()}')
+        # Initialize guidance with zeros for all dimensions and apply SDF gradients to position dimensions
+        guidance = torch.zeros_like(x_unnormalized)
+        guidance[..., :3] = running_avg_sdf_gradient  # Use positions.grad instead of sdf_values.grad
+        
+        return guidance
+
+    def _calculate_smooth_guidance(self, x_unnormalized):
+        # Extract position coordinates and create a copy that requires gradients
+        positions = x_unnormalized[..., :3].detach().clone().requires_grad_(True)
+        
+        # Calculate the smoothness objective: sum of squared accelerations
+        if positions.shape[1] >= 3:
+            velocities = positions[:, 1:] - positions[:, :-1]
+            accelerations = velocities[:, 1:] - velocities[:, :-1]
+            smoothness_objective = torch.sum(torch.norm(accelerations, dim=-1)**2)
+            # Calculate gradients through automatic differentiation
+            smoothness_objective.backward()
+            # Get the gradient
+            smooth_grad = positions.grad
+            # Initialize guidance with zeros for all dimensions
+            guidance = torch.zeros_like(x_unnormalized)
+            guidance[..., :3] = -smooth_grad  # Negative to minimize the objective
+            
+            return guidance
+        else:
+            return torch.zeros_like(x_unnormalized)
+            
+
+class GuideManagerSTOMP(GuideManager):
+    class LocalDirectionMode(Enum):
+            WAYPOINT = 0
+            RESAMPLE_WAYPOINT = 1
+            RESAMPLE_SPLINE = 2
+
+    def __init__(self,
+                 dataset,
+                 robot_model,
+                 tensor_args,
+                 clip_grad=False, clip_grad_rule='norm',
+                 max_grad_norm=1.0, max_grad_value=1.0,
+                 guidance_weight=0.15
+                 ):
+        super().__init__(dataset,
+                         tensor_args,
+                         clip_grad, clip_grad_rule, 
+                         max_grad_norm, max_grad_value,
+                         guidance_weight)
+        mode_int = 1
+        self._local_direction_mode = self.LocalDirectionMode(mode_int)
+
+        self._point_cloud = None
+        self._max_distance = 0.20
+        self._sdf_model = PointCloud_CSDF(sphere_radius=0.05, max_distance=None, device=self.device, pcd=None)
+        # print("GUIDANCE STRENGTH: ", self.guidance_weight)
+        self._advanced_kinematics_solver = AdvancedKinematicsSolver(robot_model)
+        # self._selected_links = ["panda_link0", "panda_link1", "panda_link2", "panda_link3", 
+                        #    "panda_link4", "panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand"]
+        # self._selected_links = ["panda_link1", "panda_link2", "panda_link3", "panda_link4", 
+        #                         "panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand"]
+        self._selected_links = ["panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand", "panda_leftfinger", "panda_rightfinger"]
+        # Dictionary to store debug values that can be accessed from outside
+        self.debug_state = []
+    
+    def reset_debug_state(self):
+        self.debug_state = []
+    
+    def forward(self, x_normalized, get_cost_rank=False, get_debug_info=True):
+        x_norm = x_normalized.clone() # ⚠️ IMPORTANT: x should be joint states, not end-effector poses
+        x = self.dataset.unnormalize_trajectory(x_norm)  # x shape: [batch, length, joint_dim], joint_dim = 9
+        restructured_data = self._get_cartesian_positions(x)
+        sdf_cost = self._calculate_sdf_cost(restructured_data['all_sphere_poses'], restructured_data['all_sphere_radii'])  # [batch, seq_len, sphere_num]
+        # filter out the cost of the sphere that is not in the selected links
+        sdf_cost = self._filter_cost_by_mask(sdf_cost, restructured_data['all_sphere_mask']) # [batch, seq_len, sphere_num]
+        sdf_gradient = self._calculate_mixed_gradient(sdf_cost, x, alpha_global=3.0, sliding_window = 3, max_window_nums=5)
+        guidance = self.guidance_weight*sdf_gradient #delta value does not need normalized. delta and absolute does not share same normalizer
+        x_after_guidance = x + guidance
+        guidance_norm = self.dataset.normalizer.normalize(x_after_guidance, self.dataset.data_key_traj) - x_norm
+        
+        # ------ Debug info ------
+        if get_cost_rank:
+            # Sort batch indices by total cost (sum over all timesteps and spheres)
+            cost_total = sdf_cost.sum(dim=(1, 2))  # [batch]
+            self.sdf_cost_sorted_indices = torch.argsort(cost_total, dim=0)  # ascending order
+
+        if get_debug_info:
+            # Make sure to not modify x; use a copy for x_after_guidance
+            x_after_guidance = x.detach().cpu().numpy().copy() + guidance.detach().cpu().numpy().copy()
+            temp_data =self._get_cartesian_positions(x_after_guidance)
+            debug_entry = DebugEntry(
+                sphere_poses=restructured_data['all_sphere_poses'],
+                sphere_radii=restructured_data['all_sphere_radii'],
+                sdf_costs=sdf_cost,
+                spheres_after_guidance=temp_data['all_sphere_poses'], 
+                sampled_spheres=self.debug_local_samples_spheres,
+            )
+
+            self.debug_state.append(debug_entry)
+            # print("sdf_gradient shape: ", sdf_gradient.shape)
+            # print("SDF gradient statistics:")
+            # print(f"Shape: {sdf_gradient.shape}")
+            # print(f"Mean: {sdf_gradient.mean().item():.4f}")
+            # print(f"Std: {sdf_gradient.std().item():.4f}")
+            # print(f"Min: {sdf_gradient.min().item():.4f}")
+            # print(f"Max: {sdf_gradient.max().item():.4f}")
+            # print(f"Non-zero elements: {(sdf_gradient != 0).sum().item()}")
+        #---- end debug info ------
+        return guidance_norm 
+
+    def update_observation(self, obs):
+        self._point_cloud = obs['point_cloud']
+        self._sdf_model.update_pcd(self._point_cloud)
+    
+    def _get_cartesian_positions(self, joint_states): 
+        # what is the best way to get end-effector poses? shall I wrap the restructure function in this?
+        if isinstance(joint_states, torch.Tensor): # this is not efficient, but let's keep it for now for MVP
+            joint_states = joint_states.detach().cpu().numpy()
+        sphere_poses_list = []
+        sphere_radii_list = []
+        for b in range(joint_states.shape[0]):
+            batch_sphere_poses = []
+            batch_sphere_radii = []
+            for t in range(joint_states.shape[1]):
+                sphere_poses, sphere_radii = self._advanced_kinematics_solver.calculate_all_sphere_positions_and_radii(joint_states[b, t, :])
+                batch_sphere_poses.append(sphere_poses)
+                batch_sphere_radii.append(sphere_radii)
+            sphere_poses_list.append(batch_sphere_poses)
+            sphere_radii_list.append(batch_sphere_radii)
+                
+        restructured_data = self._restructure_sphere_data(sphere_poses_list, sphere_radii_list)
+        return restructured_data
+
+    def _filter_cost_by_mask(self, cost, sphere_mask):
+        filtered_cost = cost * sphere_mask
+        return filtered_cost
+
+    def _calculate_local_direction_from_resample_spline(self, 
+                                                        joint_states: torch.Tensor, 
+                                                        window_indices: torch.Tensor,
+                                                        alpha_local: float = 3.0):
+        """
+        Calculate the local direction for each timestep using a spline.
+        window_indices: [k, window_size]
+        joint_states: [batch, seq_len, joint_dim]
+        """
+        def batch_window_perturb_sample_spline(joint_states, window_indices, num_samples=8, perturb_std=0.1, num_interp=10):
+            """
+            For each [batch, window], perturb p2 of window and sample points on Catmull-Rom spline.
+            Args:
+                joint_states: [batch, seq_len, joint_dim]
+                window_indices: [num_windows, window_size]
+                num_samples: int, number of p2 perturbations per window
+                perturb_std: float, std of the perturbation
+                num_interp: int, number of sampled points per spline
+            Returns:
+                sampled_points: [batch, num_windows, num_samples, num_interp, joint_dim]
+                perturbed_controls: [batch, num_windows, num_samples, 4, joint_dim]
+            """
+            batch_size, seq_len, joint_dim = joint_states.shape
+            num_windows, window_size = window_indices.shape
+            
+            # Gather window control points: [batch, num_windows, window_size, joint_dim]
+            idx = einops.repeat(window_indices, 'w s -> b w s d', b=batch_size, d=joint_dim)
+            # Use gather to select each joint dim independently
+            joint_states_exp = einops.repeat(joint_states, 'b t d -> b w t d', w=num_windows)
+            window_points = torch.gather(joint_states_exp, 2, idx) # [batch, num_windows, window_size, joint_dim]
+            # print("window_points shape:", window_points.shape)
+
+            # Expand for batch p2 perturb: [batch, num_windows, num_samples, window_size, joint_dim]
+            controls = einops.repeat(window_points, 'b w t d -> b w n t d', n=num_samples)
+            noise = torch.randn(batch_size, num_windows, num_samples, joint_dim, device=joint_states.device) * perturb_std
+            # print("controls shape:", controls.shape)
+            # print("noise shape:", noise.shape)
+            controls = controls.clone() # [batch, num_windows, num_samples, window_size, joint_dim]
+            controls[:, :, :, 2, :] += noise  # Only perturb p2
+
+            # Interpolation
+            t = torch.linspace(0, 1, num_interp, device=joint_states.device)
+            t = einops.rearrange(t, 'i -> 1 1 1 i 1')  # [1, 1, 1, num_interp, 1]
+
+            # Controls: [batch, num_windows, num_samples, window_size, joint_dim]
+            p0 = einops.rearrange(controls[:, :, :, 0, :], 'b w n d -> b w n 1 d')
+            p1 = einops.rearrange(controls[:, :, :, 1, :], 'b w n d -> b w n 1 d')
+            p2 = einops.rearrange(controls[:, :, :, 2, :], 'b w n d -> b w n 1 d')
+            p3 = einops.rearrange(controls[:, :, :, 3, :], 'b w n d -> b w n 1 d')
+
+            a = 2 * p1
+            b = -p0 + p2
+            c = 2*p0 - 5*p1 + 4*p2 - p3
+            d = -p0 + 3*p1 - 3*p2 + p3
+
+            sampled_points = 0.5 * (a + b * t + c * t**2 + d * t**3)  # [batch, num_windows, num_samples, num_interp, joint_dim]
+
+            return sampled_points, controls, window_points
+        
+        def compute_sampled_gradient(sampled_points, cost, window_joint_states, alpha=4.0):
+            """
+            Args:
+                sampled_points: [batch, num_windows, num_samples, num_interp, joint_dim]
+                cost: [batch, w, n, t]
+                window_joint_states: [batch, window_num, window_size, joint_dim]
+                alpha: softmin temp
+            Returns:
+                grad: [batch, w, t, joint_dim]
+            """
+            # delta: each sample's difference to the current window's mean
+            # sampled_points: [batch, w, n, t, joint_dim]
+            # mean_joint_states_window: [batch, w, t, joint_dim] -> expand to match n
+            batch, joint_dim = sampled_points.shape[0], sampled_points.shape[-1]
+            n = sampled_points.shape[2]
+            num_windows = window_joint_states.shape[1]
+            t = sampled_points.shape[3]
+            delta = torch.zeros(batch, num_windows, n, t, joint_dim, device=sampled_points.device)
+            delta[..., 1, :] = sampled_points[..., 0, :] - window_joint_states[..., 1, :].unsqueeze(2).expand(-1, -1, n, -1)
+            delta[..., 2, :] = sampled_points[..., -1, :] - window_joint_states[..., 2, :].unsqueeze(2).expand(-1, -1, n, -1)
+            
+            # cost softmin over n (perturbations)
+            # cost: [batch, w, n, t]
+            cost_norm = cost - cost.amin(dim=2, keepdim=True)        # min-norm over n
+            weights = torch.softmax(-alpha * cost_norm, dim=2)       # [batch, w, n, t]
+            weights = weights.unsqueeze(-1)                          # [batch, w, n, t, 1]
+            grad = (weights * delta).sum(dim=2)                      # [batch, w, t, joint_dim]
+            return grad
+        
+        # mean_joint_states = joint_states.mean(dim=0).unsqueeze(0)  # [1, seq_len, joint_dim]
+        pertube_sample_num = 5
+        num_interp = window_indices.shape[1] # don't make it too complex..
+        # sampled_points, controls, window_points = batch_window_perturb_sample_spline(
+        #     mean_joint_states, 
+        #     window_indices, 
+        #     num_samples=pertube_sample_num, 
+        #     num_interp=num_interp
+        # )
+        sampled_points, controls, window_points = batch_window_perturb_sample_spline(
+            joint_states, 
+            window_indices, 
+            num_samples=pertube_sample_num, 
+            num_interp=num_interp,
+            perturb_std=0.2
+        )
+        # print("window_points shape:", window_points.shape)
+        # print("sampled_points shape:", sampled_points.shape)
+        # print("controls shape:", controls.shape)
+        sampled_points = einops.rearrange(sampled_points, 'b w n t d -> b (w n t) d')
+        restructured_data = self._get_cartesian_positions(sampled_points)
+        sdf_cost = self._calculate_sdf_cost(restructured_data['all_sphere_poses'], restructured_data['all_sphere_radii'])
+        # filter out the cost of the sphere that is not in the selected links
+        sdf_cost = self._filter_cost_by_mask(sdf_cost, restructured_data['all_sphere_mask']).sum(dim=-1) #[batch, seq_len]
+        cost = einops.rearrange(sdf_cost, 'b (w n t) -> b w n t', w=window_indices.shape[0], n = pertube_sample_num, t = num_interp)
+        sampled_points = einops.rearrange(sampled_points, 'b (w n t) d -> b w n t d', w=window_indices.shape[0], n = pertube_sample_num, t = num_interp)
+        
+        # print("cost shape:", cost.shape)
+        # softmax gradient over the cost
+        grad = compute_sampled_gradient(sampled_points, cost, window_points, alpha=1.0)
+        # print("grad shape:", grad.shape)
+        # print("grad:", grad[0,0,:])
+
+        # transform gard to original joint index
+        # grad: [batch, num_windows, num_interp, joint_dim]
+        # window_indices: [num_windows, num_interp]
+        batch, num_windows, num_interp, joint_dim = grad.shape
+        seq_len = joint_states.shape[1]
+
+        # Expand window_indices for batch和joint_dim
+        grad_flat = einops.rearrange(grad, 'b w t d -> b (w t) d')
+        # print("grad_flat shape:", grad_flat.shape)
+        # print("grad flat value:", grad_flat)
+        idx_flat = einops.rearrange(window_indices, 'w t -> (w t)')
+        idx_flat = einops.repeat(idx_flat, 'n -> b n d', b=batch, d=joint_dim)
+        
+        grad_full = torch.zeros(batch, seq_len, joint_dim, device=grad.device)
+        grad_full = grad_full.scatter_add(dim=1, index=idx_flat, src=grad_flat)
+
+        return grad_full
+    
+    def _calculate_local_direction_from_resample_waypoint(self,
+                                                          joint_states: torch.Tensor, 
+                                                          window_indices: torch.Tensor,
+                                                          alpha_local: float = 3.0,
+                                                          perturb_std: float = 0.2):
+        """Calculate the local direction for each timestep using a waypoint."""
+        def batch_perturb_and_resample(joint_states, window_indices, perturb_plan, perturb_std=0.2, num_interp=10):
+            # sample from polygon, not gaussian
+            batch_size, seq_len, joint_dim = joint_states.shape
+            num_windows, window_size = window_indices.shape
+            # assert window_size == 4
+            num_samples = len(perturb_plan)
+            
+            # Gather window control points: [batch, num_windows, window_size, joint_dim]
+            idx = einops.repeat(window_indices, 'w s -> b w s d', b=batch_size, d=joint_dim)
+            # Use gather to select each joint dim independently
+            joint_states_exp = einops.repeat(joint_states, 'b t d -> b w t d', w=num_windows)
+            window_points = torch.gather(joint_states_exp, 2, idx) # [batch, num_windows, window_size, joint_dim]
+            # print("window_points shape:", window_points.shape)
+
+            # Joint sampling, batch expansion and perturbation: [batch, num_windows, num_samples, window_size, joint_dim]
+            # perturbed_windows = einops.repeat(window_points, 'b w t d -> b w n t d', n=num_samples)
+            ## noise = torch.randn(batch_size, num_windows, num_samples, window_size, joint_dim, device=joint_states.device) * perturb_std
+            ## perturbed_windows = perturbed_windows + noise  # [batch, num_windows, num_samples, window_size, joint_dim]
+            # perturbed_windows = trust_region_perturbations(window_points, perturb_std, perturb_plan)
+
+            # Cartesian sampling
+            perturbed_windows = trust_region_cartesian_perturbations(window_points, perturb_std, perturb_plan)
+
+            # Resampling/interpolation:
+            # Reshape to (batch * num_windows * num_samples, window_size, joint_dim) for efficient interpolation
+            flat = perturbed_windows.reshape(-1, window_size, joint_dim)  # [BWN, window_size, joint_dim]
+            flat_up = F.interpolate(flat.transpose(1, 2), size=num_interp, mode='linear', align_corners=True)
+            flat_up = flat_up.transpose(1, 2)  # [BWN, num_interp, joint_dim]
+            resampled = flat_up.view(batch_size, num_windows, num_samples, num_interp, joint_dim)
+            return resampled, perturbed_windows, window_points  # [batch, num_windows, num_samples, num_interp, joint_dim], [batch, num_windows, num_samples, window_size, joint_dim]
+
+        def trust_region_perturbations(window_points, std, perturb_plan):
+            num_samples = len(perturb_plan)
+            batch, num_windows, window_size, joint_dim = window_points.shape
+            perturbed = window_points.unsqueeze(2).expand(-1, -1, num_samples, -1, -1).clone()
+
+            for sample_idx, plan in enumerate(perturb_plan):
+                for idx in range(1, window_size-1):  # skip first and last
+                    for joint_id, sign in plan:
+                        perturbed[:, :, sample_idx, idx, joint_id] += sign * std
+            return perturbed
+
+
+        def trust_region_cartesian_perturbations(window_points, std, cartesian_perturb_dirs):
+            """
+            window_points: [batch, num_windows, window_size, joint_dim]
+            num_samples: int
+            std: float
+            return: [batch, num_windows, num_samples, window_size, joint_dim]
+            """
+            dirs = np.array([
+                    [1, 1, 1], [1, 1, -1], [1, -1, 1], [1, -1, -1],
+                    [-1, 1, 1], [-1, 1, -1], [-1, -1, 1], [-1, -1, -1]
+                ], dtype=np.float32)
+            
+            num_dirs = dirs.shape[0]
+            b, w, t, d = window_points.shape
+
+            # Move to CPU/numpy for IK and FK
+            window_points_np = window_points.detach().cpu().numpy().reshape(-1, d)  # (N, d)
+            perturbed_qpos_list = []
+
+            for qpos in window_points_np:
+                self._advanced_kinematics_solver.calculate_forward_kinematics(qpos)
+                cartesian_pose = self._advanced_kinematics_solver.get_all_link_pose()["panda_hand"]
+                # Perturb in cartesian space
+                perturbed_cartesian_poses = perturb_cartesian_pose(cartesian_pose, std, dirs=dirs)  # (num_dirs, 7)
+                # IK for each perturbed pose
+                perturbed_qpos = []
+                for pose in perturbed_cartesian_poses:
+                    qpos_ik = self._advanced_kinematics_solver.compute_ik(pose, initial_qpos=qpos)
+                    if qpos_ik is None:
+                        qpos_ik = qpos  # fallback
+                    perturbed_qpos.append(qpos_ik)
+                perturbed_qpos = np.stack(perturbed_qpos, axis=0)  # (num_dirs, d)
+                perturbed_qpos_list.append(perturbed_qpos)
+        
+            # Stack and reshape
+            perturbed_windows = np.stack(perturbed_qpos_list, axis=0)  # (b * w * t, num_dirs, d)
+            perturbed_windows = perturbed_windows.reshape(b, w, t, num_dirs, d)
+            perturbed_windows = np.transpose(perturbed_windows, (0, 1, 3, 2, 4))  # → [b, w, num_dirs, t, d]
+            perturbed_windows = torch.tensor(perturbed_windows, device=window_points.device, dtype=window_points.dtype)
+            return perturbed_windows
+        
+        def perturb_cartesian_pose(cartesian_pose, edge_len, dirs = None):
+            """
+            Generate 8 perturbed poses that form a cube around the input pose.
+            Args:
+                cartesian_pose: Pose object or np.ndarray with fields .p (position [3]) and .q (quaternion [4])
+                edge_len: float, total edge length of the cube
+            
+            Returns:
+                perturbed_poses: [batch, num_dirs, 7]  # 7 for (x, y, z, qw, qx, qy, qz)
+            """
+            if dirs is None:
+                dirs = np.array([
+                    [1, 1, 1], [1, 1, -1], [1, -1, 1], [1, -1, -1],
+                    [-1, 1, 1], [-1, 1, -1], [-1, -1, 1], [-1, -1, -1]
+                ], dtype=np.float32)
+            
+            dirs = dirs / np.linalg.norm(dirs, axis=1, keepdims=True)  # normalize
+            half_diag = edge_len / 2.0
+            displacements = dirs * half_diag  # length from center to each corner
+
+            pos = cartesian_pose.p  # [3]
+            quat = cartesian_pose.q  # [4]
+
+            pos_perturbed = pos[None, :] + displacements  # [8, 3]
+            # Add small random perturbations to quaternions
+            quat_noise = np.random.normal(0, 0.15, (8, 4))  # Small Gaussian noise
+            quat_perturbed = quat[None, :] + quat_noise  # [8, 4]
+            # Normalize quaternions to ensure they remain valid
+            quat_norms = np.linalg.norm(quat_perturbed, axis=1, keepdims=True)
+            quat_perturbed = quat_perturbed / quat_norms
+
+            perturbed_poses = np.concatenate([pos_perturbed, quat_perturbed], axis=-1)  # [8, 7]
+            return perturbed_poses
+
+        def compute_sampled_gradient(sampled_points, cost, window_joint_states, alpha=4.0):
+            cost_per_sample = cost.sum(dim=-1) # [batch, num_windows, num_samples]
+            # 2. Softmin over samples (n) in each window
+            # cost_norm = cost_per_sample - cost_per_sample.amin(dim=2, keepdim=True)
+            cost_norm = (cost_per_sample - cost_per_sample.mean(dim=2, keepdim=True)) / (cost_per_sample.std(dim=2, keepdim=True) + 1e-8)
+            weights = torch.softmax(-alpha * cost_norm, dim=2)  # [batch, num_windows, num_samples]
+            
+            # cost_per_sample shape: torch.Size([batch, num_windows, pertube_nums])
+            # resampled_sampled_points shape: torch.Size([batch, num_windows, pertube_nums, window_length , 9])
+            # window_joint_states shape: torch.Size([batch, num_windows, window_length , 9])
+            delta = sampled_points - window_joint_states.unsqueeze(2)  # [batch, num_windows, num_samples, window_length, joint_dim]
+            # print("cost norm", cost_norm)
+            # print("weights", weights)
+            # print("delta shape", delta.shape)
+            # print("delta[0,0,0,:]", delta[0,0,0,:])
+            # print("delta[0,0,1,:]", delta[0,0,1,:])
+            # grad_window = delta[:,:,0,:,:]
+            weights_expand = weights.unsqueeze(-1).unsqueeze(-1)  # [batch, num_windows, num_samples, 1, 1]
+            grad_window = (weights_expand * delta).sum(dim=2)  # [batch, num_windows, window_length, joint_dim]
+            return grad_window
+            
+        def reinterpolate_sampled_points(sampled_points, window_indices, num_interp=10):
+            # sampled_points: [batch, num_windows, num_samples, num_interp, joint_dim]
+            # window_indices: [num_windows, num_interp]
+            # sampled_points: [b, w, n, num_interp, d]
+            b, w, n, num_interp, d = sampled_points.shape
+            target_t = window_indices.shape[1]
+            # Reshape for interpolation: [b*w*n, d, num_interp]
+            sampled_points_reshaped = sampled_points.permute(0, 1, 2, 4, 3).reshape(-1, d, num_interp)
+            # Interpolate along t dimension
+            sampled_points_interp = F.interpolate(
+                sampled_points_reshaped, 
+                size=target_t, 
+                mode='linear', 
+                align_corners=True
+            )
+            # Reshape back: [b, w, n, d, target_t]
+            sampled_points_interp = sampled_points_interp.reshape(b, w, n, d, target_t).permute(0, 1, 2, 4, 3)
+            return sampled_points_interp
+
+        # def resample_to_original_window_size(sampled_points, window_points, window_indices):
+        # 
+        
+        # perturb_plan = [
+        #     [(3, +2), (6, -3)],  # joint3 +2, joint6 -2
+        #     [(3, -2), (5, -3)],  # joint3 -2, joint5 -2
+        #     [(0, +1), (3, +2)],  # joint0 +1, joint3 +2
+        #     [(0, -1), (4, +3)],  # joint0 -1, joint4 +2
+        #     [(1, +1), (3, -2)],  # joint1 +1, joint3 -2
+        #     [(1, -1), (5, +3)],  # joint1 -1, joint5 +2
+        #     [(2, +1), (4, -3)],  # joint2 +1, joint4 -2
+        #     [(2, -1), (6, +3)],  # joint2 -1, joint6 +2
+        # ]
+
+        # perturb_plan = [ #joint
+        #     [(0, +0.5)], [(0, -0.5)], [(1, +1.0)], [(1, -1.0)], [(2, +1.0)], [(2, -1.0)],
+        #     [(3, +2.0)], [(3, -2.0)], [(4, +3.0)], [(4, -3.0)], 
+        #     [(5, +4.0)], [(5, -4.0)], [(6, +4.0)], [(6, -4.0)],
+        # ]
+        num_interp = 4 # don't make it too complex..
+
+        perturb_plan = np.array([ #cartesian
+                    [1, 1, 1], [1, 1, -1], [1, -1, 1], [1, -1, -1],
+                    [-1, 1, 1], [-1, 1, -1], [-1, -1, 1], [-1, -1, -1]
+                ], dtype=np.float32)
+    
+        sampled_points, perturbed_windows, window_points = batch_perturb_and_resample(
+            joint_states, 
+            window_indices, 
+            perturb_plan,
+            num_interp=num_interp,
+            perturb_std=perturb_std
+        )
+        num_samples = len(perturb_plan)
+        # print("perturbed_windows shape:", perturbed_windows.shape)
+        # print("sampled_points shape:", sampled_points.shape)
+        sampled_points = einops.rearrange(sampled_points, 'b w n t d -> b (w n t) d') # [batch, (w pertube_sample_num, num_interp), joint_dim]
+        restructured_data = self._get_cartesian_positions(sampled_points)
+        sdf_cost = self._calculate_sdf_cost(restructured_data['all_sphere_poses'], restructured_data['all_sphere_radii'])
+        # filter out the cost of the sphere that is not in the selected links
+        sdf_cost = self._filter_cost_by_mask(sdf_cost, restructured_data['all_sphere_mask']).sum(dim=-1) #[batch, seq_len]
+        
+        cost = einops.rearrange(sdf_cost, 'b (w n t) -> b w n t', w=window_indices.shape[0], n = num_samples, t = num_interp)
+        # print("cost", cost)
+        sampled_points = einops.rearrange(sampled_points, 'b (w n t) d -> b w n t d', w=window_indices.shape[0], n = num_samples, t = num_interp) #this need to be resampled to ori
+        resampled_sampled_points = reinterpolate_sampled_points(sampled_points, window_indices, num_interp=num_interp)
+        # # print("resampled_sampled_points shape:", resampled_sampled_points.shape)
+        grad = compute_sampled_gradient(resampled_sampled_points, cost, window_points, alpha=8.0)
+        # grad = compute_sampled_gradient(sampled_points, cost, window_points, alpha=4.0)
+        # grad = sampled_points[:,:,0,:,:] - window_points
+
+        # print("local grad shape:", grad.shape)
+        # print("local grad value:", grad.sum(dim=3))
+        self.debug_local_samples_spheres = restructured_data['all_sphere_poses']
+        # transform gard to original joint index
+        # grad: [batch, num_windows, num_interp, joint_dim]
+        # window_indices: [num_windows, num_interp]
+        batch, num_windows, num_interp, joint_dim = grad.shape
+        seq_len = joint_states.shape[1]
+
+        # # Expand window_indices for batch and joint_dim
+        grad_flat = einops.rearrange(grad, 'b w t d -> b (w t) d')
+        # print("grad_flat shape:", grad_flat.shape)
+        idx_flat = einops.rearrange(window_indices, 'w t -> (w t)')
+        idx_flat = einops.repeat(idx_flat, 'n -> b n d', b=batch, d=joint_dim)
+        
+        grad_full = torch.zeros(batch, seq_len, joint_dim, device=grad.device)
+        grad_full = grad_full.scatter_add(dim=1, index=idx_flat, src=grad_flat)
+        # Compute statistics along all except the last dimension (joint_dim)
+        # mean_per_joint = grad_full.mean(dim=(0,1))  # [joint_dim]
+        # std_per_joint  = grad_full.std(dim=(0,1))   # [joint_dim]
+        # min_per_joint  = grad_full.amin(dim=(0,1))  # [joint_dim]
+        # max_per_joint  = grad_full.amax(dim=(0,1))  # [joint_dim]
+
+        # print("Mean per joint:", mean_per_joint)
+        # print("Std per joint:", std_per_joint)
+        # print("Min per joint:", min_per_joint)
+        # print("Max per joint:", max_per_joint)
+        # print("grad_full shape:", grad_full.shape)
+                
+        return grad_full
+        
+        # print("cost shape:", cost.shape)
+        # raise NotImplementedError("Resample waypoint is not implemented yet")
+
+    def _calculate_local_direction_from_waypoint(self, cost: torch.Tensor, joint_states: torch.Tensor, alpha_local: float = 3.0):
+        """
+        Calculate the local direction for each timestep using a waypoint.
+        """
+        batch, seq_len = cost.shape
+        # 2. Calculate local direction for each timestep
+        local_direction_list = []
+        for t in range(seq_len):
+            # Get cost for all samples at this waypoint
+            cost_t = cost[:, t]  # [batch]
+            # Normalize: softmin, i.e. exp(-alpha_local * (cost_t - min))
+            cost_t_norm = cost_t - cost_t.min()
+            weights_local = torch.exp(-alpha_local * cost_t_norm)
+            weights_local = weights_local / (weights_local.sum() + 1e-8)  # [batch]
+            # States at current timestep
+            states_t = joint_states[:, t, :]  # [batch, joint_dim]
+            mean_state = states_t.mean(dim=0)
+            # Weighted direction relative to the mean state
+            direction = (weights_local[:, None] * (states_t - mean_state)).sum(dim=0)  # [joint_dim]
+            local_direction_list.append(direction)
+        local_direction = torch.stack(local_direction_list, dim=0)  # [seq_len, joint_dim]
+        return local_direction
+    
+    def _calculate_mixed_gradient(
+        self,
+        cost: torch.Tensor,
+        joint_states: torch.Tensor,
+        alpha_local: float = 3.0,
+        alpha_global: float = 3.0,
+        sliding_window: int = 4,
+        max_window_nums: int = 3,
+    ) -> torch.Tensor:
+        """
+        Combine full-trajectory and per-timestep weighted STOMP updates for a mixed gradient direction.
+        Args:
+            cost: [batch, seq_len, cost_per_sphere]    # tensor
+            joint_states: [batch, seq_len, joint_dim]  # tensor
+            alpha: float, softmax scaling factor
+            lambda_local: float, blending ratio for per-timestep (local) update
+        Returns:
+            grads: [seq_len, joint_dim]                # tensor
+        """
+        batch, seq_len, sphere_nums = cost.shape
+        device = cost.device
+
+        # ------ Compute GLOBAL weight for each waypoint using sliding window of costs ------
+        cost_per_sample = cost.sum(dim=-1)  # [batch, seq_len]
+        window_costs = self._sliding_window_sum(cost_per_sample, sliding_window) # TODO: assume no...multi modes (will fix)
+        
+        # Normalize using global mean
+        window_costs_norm =(window_costs - window_costs.min()) / (window_costs.max()+1e-8)
+        
+        # Compute softmax weights
+        softmax_weights = torch.exp(alpha_global * window_costs_norm)
+        global_weight = softmax_weights / (softmax_weights.sum() + 1e-8)
+        # print("window_costs_norm:", window_costs_norm)
+        # print("weights_t:", global_weight)
+        
+        # ------ Calculate local update direction ------
+        if self._local_direction_mode == self.LocalDirectionMode.RESAMPLE_SPLINE:
+            window_indices = self._get_window_indices(seq_len, sliding_window, device=device)
+            top_k_values, top_k_window_indices = self._get_top_k_windows(window_costs, window_indices, k=max_window_nums)
+            # print("top_k_values:", top_k_values)
+            # print("top_k_window_indices:", top_k_window_indices)
+            local_direction = self._calculate_local_direction_from_resample_spline(joint_states, 
+                                                                                   top_k_window_indices, 
+                                                                                   alpha_local)
+        elif self._local_direction_mode == self.LocalDirectionMode.RESAMPLE_WAYPOINT:
+            window_indices = self._get_window_indices(seq_len, sliding_window, device=device)
+            top_k_values, top_k_window_indices = self._top_k_windows_with_low_overlap(window_costs, window_indices, k=max_window_nums)
+            # print("top_k_values:", top_k_values)
+            # print("top_k_window_indices:", top_k_window_indices)
+            local_direction = self._calculate_local_direction_from_resample_waypoint(joint_states, 
+                                                                                   top_k_window_indices, 
+                                                                                   alpha_local,
+                                                                                   perturb_std=0.2)
+
+        elif self._local_direction_mode == self.LocalDirectionMode.WAYPOINT:
+            cost_per_sample = cost.sum(dim=-1)  # [batch, seq_len]
+            local_direction = self._calculate_local_direction_from_waypoint(cost_per_sample, joint_states, alpha_local)
+        else:
+            raise ValueError(f"Invalid local direction mode: {self._local_direction_mode}")
+
+        # ------ Combine local direction and global weight for each waypoint ------
+        # global_weight: [seq_len], local_direction: [seq_len, joint_dim]
+        grads = global_weight.unsqueeze(-1) * local_direction  # [seq_len, joint_dim]
+        # print("global_weight:", global_weight)
+        # grads = local_direction
+        
+        # Fix the start and end waypoints (do not update)
+        grads[:, 0, :] = 0.0
+        grads[:,-1, :] = 0.0
+
+        # print("grads.shape:", grads.shape)
+        # print("cost per sample:", cost_per_sample)
+        return grads
+    
+    def _sliding_window_sum(self, cost_per_sample, sliding_window_size):
+        # Add a channel dimension for conv1d: [batch, 1, seq_len]
+        cost = cost_per_sample.unsqueeze(1)
+        weight = torch.ones(1, 1, sliding_window_size, device=cost.device) #kernel[1,1,sliding_window]
+        # Pad both sides
+        if sliding_window_size % 2 == 1:  # odd
+            pad_left = pad_right = sliding_window_size // 2
+        else:  # even
+            pad_left = sliding_window_size // 2 - 1
+            pad_right = sliding_window_size // 2
+        cost_padded = F.pad(cost, (pad_left, pad_right), mode='constant', value=0)
+        window_sum = F.conv1d(cost_padded, weight) # output: [batch, 1, seq_len]
+        window_sum = window_sum.sum(dim=0).squeeze(0)  # sum across batch dimension[seq_len]
+        return window_sum
+    
+    def _get_window_indices(self, seq_len, window_size, fill_value=-1, device=None):
+        t = torch.arange(seq_len, device=device)  # [seq_len]
+        half = window_size // 2
+        offsets = torch.arange(-half, window_size - half, device=device).view(1, -1)
+        window_indices = t.view(-1, 1) + offsets
+        window_indices = torch.where(
+            (window_indices >= 0) & (window_indices < seq_len),
+            window_indices,
+            torch.full_like(window_indices, fill_value)
+        )
+        return window_indices  # [seq_len, window_size]
+    
+    def _smooth_grads(grads: torch.Tensor, weight: float = 0.4) -> torch.Tensor:
+        """
+        Apply a simple smoothing filter to the gradient updates by blending each waypoint with its immediate neighbors.
+        Args:
+            grads: torch.Tensor of shape [seq_len, joint_dim]
+                The gradient updates to be smoothed.
+            weight: float, optional (default=0.4)
+                Smoothing strength. 0 means no smoothing, higher values result in smoother gradients.
+        Returns:
+            grads_smooth: torch.Tensor of shape [seq_len, joint_dim]
+                The smoothed gradient updates.
+        """
+        grads_smooth = grads.clone()
+        # Smooth all inner waypoints by blending them with the ir left and right neighbors
+        grads_smooth[1:-1] = (
+            (1 - weight) * grads[1:-1]
+            + (weight / 2) * (grads[:-2] + grads[2:])
+        )
+        return grads_smooth
+            
+    def _calculate_sdf_cost(self, sphere_poses_tensor, sphere_radii_tensor):
+        assert sphere_poses_tensor.shape[:3] == sphere_radii_tensor.shape
+        batch_size, seq_len, num_spheres, _ = sphere_poses_tensor.shape
+        sphere_positions_flat = einops.rearrange(sphere_poses_tensor, 'b t s d -> b (t s) d')
+        sdf_distance_center = self._sdf_model(sphere_positions_flat)
+        sdf_distance_center = einops.rearrange(sdf_distance_center, 'b (t s) -> b t s', b=batch_size, t=seq_len)
+        # Distance from sphere surface
+        sdf_distance_surface = sdf_distance_center - sphere_radii_tensor
+
+        alpha = 8.0  
+        max_distance = self._max_distance  # should be scalar
+        clipped_distance = torch.clamp_max(sdf_distance_surface, max_distance)
+        sdf_cost = torch.exp(alpha * (max_distance - clipped_distance)) - 1
+        sdf_cost = torch.where(sdf_cost > 0, sdf_cost, torch.zeros_like(sdf_cost))
+        # print("sdf_cost mean:", sdf_cost.mean())
+        # print("sdf_cost max:", sdf_cost.max())
+        # print("sdf_cost min:", sdf_cost.min())
+
+        # # Clip at max_distance
+        # max_distance = self._max_distance  # should be scalar
+        # clipped_distance = torch.clamp_max(sdf_distance_surface, max_distance)
+        # sdf_cost = torch.clamp(max_distance - clipped_distance, min=0)
+        return sdf_cost
+
+    def _restructure_sphere_data(self, 
+                                 batch_sphere_poses_list: List[List[Dict[str, List[np.ndarray]]]], 
+                                 batch_sphere_radii_list: List[List[Dict[str, List[float]]]]):
+        batch_size = len(batch_sphere_poses_list)
+        seq_len = len(batch_sphere_poses_list[0])
+        spheres_per_link = {link: len(spheres) for link, spheres in batch_sphere_poses_list[0][0].items()}
+        total_spheres = sum(spheres_per_link.values())
+
+        def create_single_qpos_sphere(sphere_pose_list, sphere_radii_list):
+            sphere_poses = torch.zeros((seq_len, total_spheres, 3), device=self.device, dtype=self.dtype)
+            sphere_radii = torch.zeros((seq_len, total_spheres), device=self.device, dtype=self.dtype)
+            for t in range(seq_len):
+                idx = 0
+                for link_name, spheres in sphere_pose_list[t].items():
+                    n = len(spheres)
+                    if n == 0:
+                        continue
+                    radii = sphere_radii_list[t][link_name]
+                    assert n == len(radii), f"Mismatch: {n} spheres vs {len(radii)} radii for {link_name} at t={t}"
+                    sphere_poses[t, idx:idx+n] = torch.from_numpy(np.stack(spheres)).to(device=self.device, dtype=self.dtype)
+                    sphere_radii[t, idx:idx+n] = torch.tensor(radii, device=self.device, dtype=self.dtype)
+                    idx += n
+                assert idx == total_spheres, f"Total spheres mismatch at t={t}"
+            return sphere_poses, sphere_radii
+
+        def create_sphere_mask(sphere_pose_list):
+            mask = torch.zeros((seq_len, total_spheres), device=self.device, dtype=torch.bool)
+            for t in range(seq_len):
+                idx = 0
+                for link_name, spheres in sphere_pose_list[t].items():
+                    n = len(spheres)
+                    if n == 0:
+                        continue
+                    if hasattr(self, '_selected_links') and link_name in self._selected_links:
+                        mask[t, idx:idx+n] = True
+                    idx += n
+                assert idx == total_spheres, f"Total spheres mismatch at t={t}"
+            return mask
+
+        all_sphere_poses_tensor = torch.zeros((batch_size, seq_len, total_spheres, 3), device=self.device, dtype=self.dtype)
+        all_sphere_radii_tensor = torch.zeros((batch_size, seq_len, total_spheres), device=self.device, dtype=self.dtype)
+        # Create the mask only once, using the first batch's structure
+        mask = create_sphere_mask(batch_sphere_poses_list[0])
+        all_sphere_mask_tensor = mask.unsqueeze(0).expand(batch_size, -1, -1).clone()
+
+        for b in range(batch_size):
+            poses, radii = create_single_qpos_sphere(batch_sphere_poses_list[b], batch_sphere_radii_list[b])
+            all_sphere_poses_tensor[b] = poses
+            all_sphere_radii_tensor[b] = radii
+            # No need to recompute mask for each batch
+
+        return {
+            'all_sphere_poses': all_sphere_poses_tensor,
+            'all_sphere_radii': all_sphere_radii_tensor,
+            'all_sphere_mask': all_sphere_mask_tensor,
+        }
+
+    def _top_k_windows_with_low_overlap(self, window_costs: torch.Tensor, 
+                                   window_indices: torch.Tensor, 
+                                   k: int = 3):
+        """
+        Returns up to top k non-overlapping windows (no two windows share more than 1 index).
+        """
+        valid_mask = (window_indices >= 0).all(dim=1)
+        valid_costs = window_costs[valid_mask]
+        valid_indices = window_indices[valid_mask]  # [num_valid, window_size]
+
+        # Sort windows by cost descending (or ascending as you need)
+        sorted_costs, sorted_idx = torch.topk(valid_costs, min(len(valid_costs), k*4))  # oversample for filter
+        sorted_indices = valid_indices[sorted_idx]
+
+        selected = []
+        selected_indices = []
+
+        for idx, indices in enumerate(sorted_indices):
+            indices_set = set(indices.tolist())
+            # Check overlap with all previously selected
+            overlap = False
+            for prev in selected_indices:
+                prev_set = set(prev.tolist())
+                if len(indices_set & prev_set) > 1:
+                    overlap = True
+                    break
+            if not overlap:
+                selected.append(sorted_costs[idx])
+                selected_indices.append(indices)
+                if len(selected) == k:
+                    break
+
+        if selected:
+            return (torch.stack(selected), torch.stack(selected_indices))
+        else:
+            # No valid windows
+            return (
+                torch.empty(0, dtype=window_costs.dtype, device=window_costs.device),
+                torch.empty(0, window_indices.shape[1], dtype=window_indices.dtype, device=window_indices.device)
+            )
+
+    def _get_top_k_windows(self, window_costs: torch.Tensor, 
+                       window_indices: torch.Tensor, 
+                       k: int = 3):
+        """
+        Returns top k windows where all indices are in [0, seq_len).
+        Handles cases where invalid indices may be -1 or any other out-of-bounds value.
+        """
+        # valid_mask is True for windows where all indices are valid
+        valid_mask = (window_indices >= 0).all(dim=1)
+        # print("valid_mask:", valid_mask)
+        # print("window_indices.shape:", window_indices.shape)
+        # print("window_costs.shape:", window_costs.shape)
+        valid_costs = window_costs[valid_mask]
+        valid_indices = window_indices[valid_mask]
+
+        if valid_costs.numel() == 0:
+            return (
+                torch.empty(0, dtype=window_costs.dtype, device=window_costs.device),
+                torch.empty(0, *window_indices.shape[1:], dtype=window_indices.dtype, device=window_indices.device)
+            )
+        # Top-k over valid windows only
+        top_k = min(k, len(valid_costs))
+        top_k_values, top_k_idx = torch.topk(valid_costs, top_k)
+        top_k_window_indices = valid_indices[top_k_idx]
+        return top_k_values, top_k_window_indices
+
+    def reset(self):
+        self.reset_debug_state()
+
+class TrajectoryRanker():
+    def __init__(self,
+                 dataset,
+                 robot_model,
+                 get_current_qpos,
+                 tensor_args):
+        """ Initialize the TrajectoryRanker with dataset and tensor arguments."""
+        self.dataset = dataset
+        self.device = tensor_args['device']
+        self.dtype = tensor_args['dtype']
+        self._point_cloud = None
+        self._max_distance = 0.20
+        self._sdf_model = PointCloud_CSDF(sphere_radius=0.05, 
+                    max_distance=None, 
+                    device=self.device, 
+                    pcd=None)
+        self._kin_solver = AdvancedKinematicsSolver(robot_model)
+        self._get_current_qpos = get_current_qpos
+        # self._selected_links = ["panda_link0", "panda_link1", "panda_link2", "panda_link3", 
+        #                    "panda_link4", "panda_link5", "panda_link6", "panda_link7", 
+        #                    "panda_link8", "panda_hand", "panda_leftfinger", "panda_rightfinger"]
+        self._selected_links = ["panda_link5", "panda_link6", "panda_link7", "panda_link8", 
+                                "panda_hand", "panda_leftfinger", "panda_rightfinger"]
+
+        # Dictionary to store debug values that can be accessed from outside
+        self.debug_state = []
+    
+    def rank_trajectory(self, traj: torch.Tensor, traj_type: str = "joint"):
+        """
+        Unified entrypoint.
+        
+        Args:
+            traj: [B, T, D] normalized trajectory (D = 7 for EE, D = joint_dim for joint)
+            traj_type: "ee" or "joint"
+        Returns:
+            ranking_ids: [B]
+            ranked_traj: [B, T, D] (still in normalized space)
+        """
+        traj, joint_traj, valid_mask = self._prepare_trajectory_inputs(
+            traj,
+            traj_type
+        )
+
+        original_B = valid_mask.shape[0]
+        valid_B = traj.shape[0]  # after masking
+
+        if valid_B == 0:
+            raise RuntimeError("All trajectories invalid.")
+
+        ranking_ids, sdf_cost = self._rank_joint_trajectory_by_cost(joint_traj)
+        sorted_trajs = traj[ranking_ids]  # [valid_B, T, D]
+
+        # Repeat fill
+        repeat_factor = (original_B + valid_B - 1) // valid_B
+        tiled_trajs = sorted_trajs.repeat((repeat_factor, 1, 1))[:original_B]
+
+        return tiled_trajs
+
+        # ranking_ids, sdf_cost = self._rank_joint_trajectory_by_cost(joint_traj)
+        # print(f"Ranking IDs: {ranking_ids}")
+        # total_cost = sdf_cost.sum(dim=(1, 2))
+        # print(f"Total Cost: {total_cost[ranking_ids]}")
+        # return ranking_ids, traj[ranking_ids]  # return in normalized space
+    
+    def inpaint_trajectory(self, traj: torch.Tensor, traj_type: str = "joint", 
+                            num_windows: int = 4, top_k: int = 4):
+        """
+        Public interface: Inpaint/reassemble top-k ranked trajectories from windowed cost.
+        
+        Args:
+            traj: [B, T, D] normalized trajectory
+            traj_type: "joint" or "ee"
+        Returns:
+            recombined_trajs: [top_k**num_windows, T, D] (normalized)
+        """
+        traj, joint_traj, valid_mask = self._prepare_trajectory_inputs(
+            traj,
+            traj_type
+        )
+        
+        # Compute cost and call recombination logic
+        ranking_ids, sdf_cost = self._rank_joint_trajectory_by_cost(joint_traj)
+        recombined_trajs = self._reassemble_from_windows(traj, sdf_cost, num_windows, top_k)
+
+        # Repeat and fill
+        original_B = valid_mask.shape[0]
+        valid_B = recombined_trajs.shape[0]
+
+        if valid_B == 0:
+            raise RuntimeError("All trajectories are invalid. Cannot inpaint.")
+
+        repeat_factor = (original_B + valid_B - 1) // valid_B
+        tiled_trajs = recombined_trajs.repeat((repeat_factor, 1, 1))
+        filled_trajs = tiled_trajs[:original_B]
+        return filled_trajs
+    
+    def keep_top_k_and_expand(self, x_sorted: torch.Tensor, k: int) -> torch.Tensor:
+        """
+        Args:
+            x_sorted: [B, T, D] batch of trajectories sorted by cost (ascending)
+            k: number of top trajectories to keep
+        Returns:
+            x_top: [B, T, D] — top-k repeated to fill B if k < B
+        """
+        B = x_sorted.shape[0]
+        x_top_k = x_sorted[:k]  # [k, T, D]
+
+        if k == B:
+            return x_top_k
+
+        # Repeat to fill B
+        repeat_factor = (B + k - 1) // k  # ceil(B / k)
+        x_repeated = x_top_k.repeat((repeat_factor, 1, 1))  # [ceil* k, T, D]
+        x_padded = x_repeated[:B]  # Trim to [B, T, D]
+
+        return x_padded
+
+    def _prepare_trajectory_inputs(
+        self,
+        traj: torch.Tensor,
+        traj_type: str,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+            traj: [B, T, D] normalized
+            traj_type: "ee" or "joint"
+        Returns:
+            traj: filtered normalized traj [B', T, D]
+            joint_traj: [B', T, joint_dim]
+            valid_mask: [B]
+        """
+        traj_unnorm = self.dataset.unnormalize_trajectory(traj.clone())
+
+        if traj_type == "ee":
+            B, T = traj_unnorm.shape[:2]
+            ee_pose_ortho6d = traj_unnorm[..., :9].contiguous()
+            ee_pose_quat = transform_ortho6d_to_quat(ee_pose_ortho6d.view(B * T, 9)).view(B, T, 7)
+            joint_traj, valid_mask = self._batch_ik(ee_pose_quat)
+        elif traj_type == "joint":
+            joint_traj = traj_unnorm
+            valid_mask = torch.ones(traj.shape[0], dtype=torch.bool, device=traj.device)
+        else:
+            raise ValueError(f"Unsupported traj_type: {traj_type}")
+        if not valid_mask.any():
+            print("\033[91m[Warning] All trajectories are invalid! Forcing valid_mask = all True.\033[0m")
+            valid_mask = torch.ones_like(valid_mask, dtype=torch.bool)
+
+        return traj[valid_mask], joint_traj[valid_mask], valid_mask
+    
+    def _reassemble_from_windows(self, traj: torch.Tensor, sdf_cost: torch.Tensor, num_windows: int, top_k: int):
+        """
+        Internal logic: select top-k windows based on cost and recombine.
+
+        Args:
+            traj: [B, T, D] normalized trajectory
+            sdf_cost: [B, T, S] full sdf cost per trajectory
+        Returns:
+            recombined_trajs: [top_k**num_windows, T, D] (normalized)
+        """
+        B, T, D = traj.shape
+        window_size = T // num_windows
+        assert T % num_windows == 0, "Trajectory length T must be divisible by num_windows"
+
+        top_segments_per_window = []
+
+        for w in range(num_windows):
+            t_start = w * window_size
+            t_end = (w + 1) * window_size
+
+            window_cost = sdf_cost[:, t_start:t_end, :].sum(dim=(1, 2))  # [B]
+            top_k_ids = torch.argsort(window_cost, dim=0)[:top_k]
+            top_k_segments = traj[top_k_ids, t_start:t_end, :]  # from normalized traj
+            top_segments_per_window.append(top_k_segments)
+
+        # Cartesian product recombination
+        import itertools
+        import random
+
+        B = traj.shape[0]  # original batch size
+        all_combinations = list(itertools.product(*top_segments_per_window))
+        random.shuffle(all_combinations)  # optional: for stochasticity
+
+        # Select only the first B combinations
+        selected_combinations = all_combinations[:B]
+        recombined_trajs = [torch.cat(segments, dim=0) for segments in selected_combinations]
+        recombined_trajs = torch.stack(recombined_trajs, dim=0)  # [B, T, D]
+
+        return recombined_trajs.to(traj.device)
+    
+    def _rank_joint_trajectory_by_cost(self, joint_traj: torch.Tensor):
+        sphere_data = self._get_cartesian_positions(joint_traj)
+        sdf_cost = self._calculate_sdf_cost(sphere_data['all_sphere_poses'], sphere_data['all_sphere_radii'])
+        sdf_cost = self._filter_cost_by_mask(sdf_cost, sphere_data['all_sphere_mask'])
+        debug_entry = DebugEntry(
+                sphere_poses=sphere_data['all_sphere_poses'],
+                sphere_radii=sphere_data['all_sphere_radii'],
+                sdf_costs=sdf_cost,
+            )
+        self.debug_state.append(debug_entry)
+
+        total_cost = sdf_cost.sum(dim=(1, 2))  # [B]
+        ranking_ids = torch.argsort(total_cost, dim=0)
+        return ranking_ids, sdf_cost
+    
+    def update_observation(self, obs):
+        self._point_cloud = obs['point_cloud']
+        self._sdf_model.update_pcd(self._point_cloud)
+    
+    def reset(self):
+        self.reset_debug_state()
+
+    def reset_debug_state(self):
+        self.debug_state = []
+    
+    def _batch_ik(self, ee_trajectory: torch.Tensor, max_invalid: int = 3):
+        """
+        Convert end-effector trajectory to joint trajectory using IK.
+        Input: ee_trajectory [B, T, 7]
+        Output: joint_trajs [B, T, D]
+        """
+        ee_np = ee_trajectory.detach().cpu().numpy()
+        B, T = ee_np.shape[:2]
+        joint_dim = len(self._kin_solver.joint_indices)
+        q_trajs = np.zeros((B, T, joint_dim), dtype=np.float32)
+        invalid_ik_counts = np.zeros(B, dtype=np.int32) # count each trajectory invalid ik waypoints, if too much, remove it
+
+        for b in range(B):
+            last_valid_q = None
+            for t in range(T):
+                pose = ee_np[b, t]
+                pose = sapien.Pose(pose[:3], pose[3:])
+
+                if t == 0:
+                    # ik_sol = self._kin_solver.compute_ik(target_pose=pose, initial_qpos=self._get_current_qpos().detach().cpu().numpy())
+                    initial_qpos = self._get_current_qpos().detach().cpu().numpy().reshape(-1)
+                    ik_sol = self._kin_solver.compute_ik(target_pose=pose, initial_qpos=initial_qpos)
+                else:
+                    ik_sol = self._kin_solver.compute_ik(target_pose=pose, initial_qpos=last_valid_q)
+
+                if ik_sol is None:
+                    
+                    # print(f"[IK FAIL] Batch {b}, t={t}, pose={pose}")
+                    invalid_ik_counts[b] += 1
+                    ik_sol = last_valid_q.copy() if last_valid_q is not None else np.zeros(joint_dim, dtype=np.float32)
+
+                q_trajs[b, t] = ik_sol
+                last_valid_q = ik_sol
+                
+        valid_mask = invalid_ik_counts <= max_invalid
+        # print(f"Invalid IK counts: {invalid_ik_counts}, valid mask: {valid_mask}")
+        return torch.tensor(q_trajs, device=self.device, dtype=self.dtype), torch.from_numpy(valid_mask).to(self.device)
+    
+    def _filter_cost_by_mask(self, cost, sphere_mask):
+        filtered_cost = cost * sphere_mask
+        return filtered_cost
+
+    def _calculate_sdf_cost(self, sphere_poses_tensor, sphere_radii_tensor):
+        assert sphere_poses_tensor.shape[:3] == sphere_radii_tensor.shape
+        batch_size, seq_len, num_spheres, _ = sphere_poses_tensor.shape
+        sphere_positions_flat = einops.rearrange(sphere_poses_tensor, 'b t s d -> b (t s) d')
+        sdf_distance_center = self._sdf_model(sphere_positions_flat)
+        sdf_distance_center = einops.rearrange(sdf_distance_center, 'b (t s) -> b t s', b=batch_size, t=seq_len)
+        # Distance from sphere surface
+        sdf_distance_surface = sdf_distance_center - sphere_radii_tensor
+
+        alpha = 8.0  
+        max_distance = self._max_distance  # should be scalar
+        clipped_distance = torch.clamp_max(sdf_distance_surface, max_distance)
+        sdf_cost = torch.exp(alpha * (max_distance - clipped_distance)) - 1
+        sdf_cost = torch.where(sdf_cost > 0, sdf_cost, torch.zeros_like(sdf_cost))
+        # print("sdf_cost mean:", sdf_cost.mean())
+        # print("sdf_cost max:", sdf_cost.max())
+        # print("sdf_cost min:", sdf_cost.min())
+        return sdf_cost
+    
+    def _get_cartesian_positions(self, joint_states): 
+        # what is the best way to get end-effector poses? shall I wrap the restructure function in this?
+        if isinstance(joint_states, torch.Tensor): # this is not efficient, but let's keep it for now for MVP
+            joint_states = joint_states.detach().cpu().numpy()
+        sphere_poses_list = []
+        sphere_radii_list = []
+        for b in range(joint_states.shape[0]):
+            batch_sphere_poses = []
+            batch_sphere_radii = []
+            for t in range(joint_states.shape[1]):
+                sphere_poses, sphere_radii = self._kin_solver.calculate_all_sphere_positions_and_radii(joint_states[b, t, :])
+                batch_sphere_poses.append(sphere_poses)
+                batch_sphere_radii.append(sphere_radii)
+            sphere_poses_list.append(batch_sphere_poses)
+            sphere_radii_list.append(batch_sphere_radii)
+                
+        restructured_data = self._restructure_sphere_data(sphere_poses_list, sphere_radii_list)
+        return restructured_data
+
+    def _restructure_sphere_data(self, 
+                                 batch_sphere_poses_list: List[List[Dict[str, List[np.ndarray]]]], 
+                                 batch_sphere_radii_list: List[List[Dict[str, List[float]]]]):
+        batch_size = len(batch_sphere_poses_list)
+        seq_len = len(batch_sphere_poses_list[0])
+        spheres_per_link = {link: len(spheres) for link, spheres in batch_sphere_poses_list[0][0].items()}
+        total_spheres = sum(spheres_per_link.values())
+
+        def create_single_qpos_sphere(sphere_pose_list, sphere_radii_list):
+            sphere_poses = torch.zeros((seq_len, total_spheres, 3), device=self.device, dtype=self.dtype)
+            sphere_radii = torch.zeros((seq_len, total_spheres), device=self.device, dtype=self.dtype)
+            for t in range(seq_len):
+                idx = 0
+                for link_name, spheres in sphere_pose_list[t].items():
+                    n = len(spheres)
+                    if n == 0:
+                        continue
+                    radii = sphere_radii_list[t][link_name]
+                    assert n == len(radii), f"Mismatch: {n} spheres vs {len(radii)} radii for {link_name} at t={t}"
+                    sphere_poses[t, idx:idx+n] = torch.from_numpy(np.stack(spheres)).to(device=self.device, dtype=self.dtype)
+                    sphere_radii[t, idx:idx+n] = torch.tensor(radii, device=self.device, dtype=self.dtype)
+                    idx += n
+                assert idx == total_spheres, f"Total spheres mismatch at t={t}"
+            return sphere_poses, sphere_radii
+
+        def create_sphere_mask(sphere_pose_list):
+            mask = torch.zeros((seq_len, total_spheres), device=self.device, dtype=torch.bool)
+            for t in range(seq_len):
+                idx = 0
+                for link_name, spheres in sphere_pose_list[t].items():
+                    n = len(spheres)
+                    if n == 0:
+                        continue
+                    if hasattr(self, '_selected_links') and link_name in self._selected_links:
+                        mask[t, idx:idx+n] = True
+                    idx += n
+                assert idx == total_spheres, f"Total spheres mismatch at t={t}"
+            return mask
+
+        all_sphere_poses_tensor = torch.zeros((batch_size, seq_len, total_spheres, 3), device=self.device, dtype=self.dtype)
+        all_sphere_radii_tensor = torch.zeros((batch_size, seq_len, total_spheres), device=self.device, dtype=self.dtype)
+        # Create the mask only once, using the first batch's structure
+        mask = create_sphere_mask(batch_sphere_poses_list[0])
+        all_sphere_mask_tensor = mask.unsqueeze(0).expand(batch_size, -1, -1).clone()
+
+        for b in range(batch_size):
+            poses, radii = create_single_qpos_sphere(batch_sphere_poses_list[b], batch_sphere_radii_list[b])
+            all_sphere_poses_tensor[b] = poses
+            all_sphere_radii_tensor[b] = radii
+            # No need to recompute mask for each batch
+
+        return {
+            'all_sphere_poses': all_sphere_poses_tensor,
+            'all_sphere_radii': all_sphere_radii_tensor,
+            'all_sphere_mask': all_sphere_mask_tensor,
+        }
+    
+    
+    
+class GuideManagerJacobian(GuideManager):
+    """
+    This class is not working, keep here in case we need it later
+    """
+    def __init__(self,
+                 dataset,
+                 robot_model,
+                 tensor_args,
+                 clip_grad=False, clip_grad_rule='norm',
+                 max_grad_norm=1.0, max_grad_value=1.0,
+                 guidance_weight=0.15
+                 ):
+        super().__init__(dataset,
+                         tensor_args,
+                         clip_grad, clip_grad_rule, 
+                         max_grad_norm, max_grad_value,
+                         guidance_weight)
+        self._point_cloud = None
+        self._sdf_model = PointCloud_CSDF(sphere_radius=0.10, max_distance=0.04, device=self.device, pcd=None)
+        # print("GUIDANCE STRENGTH: ", self.guidance_weight)
+        self._advanced_kinematics_solver = AdvancedKinematicsSolver(robot_model)
+        # self._selected_links = ["panda_link0", "panda_link1", "panda_link2", "panda_link3", 
+                        #    "panda_link4", "panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand"]
+        self._selected_links = ["panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand", "panda_leftfinger", "panda_rightfinger"]
+        # self._selected_links = ["panda_hand"]
+
+        # all the links in the robot: "panda_link0", "panda_link1", "panda_link2", "panda_link3", 
+        # "panda_link4", "panda_link5", "panda_link6", "panda_link7", "panda_link8", "panda_hand"
+        # panda_leftfinger, panda_rightfinger, camera_base_link, camera_link
+        # Dictionary to store debug values that can be accessed from outside
+        self.debug_state = []
+    
+    def update_observation(self, obs):
+        self._point_cloud = obs['point_cloud']
+        self._sdf_model.update_pcd(self._point_cloud)
+    
+    def forward(self, x_normalized):
+        '''
+        In this class, x has to be joint states, not end-effector poses
+        x_normalized: [batch, length, joint_dim]
+
+        Define the pipeline before heading to submodules
+        1. calculate sdf on each sphere
+            a. calculate sphere position
+            b. get direction based on each sphere
+        2. derivative each sphere's direction to delta joint
+            a. get jacobian of each sphere based on current joint position (x)
+            b. calculate derivative
+        3. add joint derivative to each joint
+        '''
+        # start_time = time.time()
+        x_norm = x_normalized.clone() # ⚠️ IMPORTANT: x should be joint states, not end-effector poses
+        x = self.dataset.unnormalize_trajectory(x_norm)
+
+        # Convert tensor x to numpy array for sphere & jacobian calculation
+        x_np = x.detach().cpu().numpy() #TODO: this copy waste resouce, but...will find a better way after MVP works
+        # x shape: [batch, length, joint_dim], joint_dim = 9
+        # CPU version, only choose the first batch
+        sphere_poses_list = []
+        sphere_radii_list = []
+        sphere_jacobians_list = []
+        for i in range(x_np.shape[1]):
+            sphere_poses, sphere_radii = self._advanced_kinematics_solver.calculate_all_sphere_positions_and_radii(x_np[0, i, :]) # un-normalized x_norm, sample spheres
+            sphere_jacobians = self._advanced_kinematics_solver.calculate_all_sphere_jacobian(x_np[0, i, :])
+            sphere_poses_list.append(sphere_poses)
+            sphere_radii_list.append(sphere_radii)
+            sphere_jacobians_list.append(sphere_jacobians)
+        
+        self.debug_state.append({
+            'sphere_poses': sphere_poses_list,
+            'sphere_radii': sphere_radii_list,
+        })
+
+        joint_guidance = self._calculate_sdf_guidance(sphere_poses_list, sphere_radii_list, sphere_jacobians_list) #[batch, length, joint_dim]
+
+        # Calculate statistics for each joint dimension
+        joint_guidance_mean = torch.mean(joint_guidance, dim=(0,1))  # Average across batch and time
+        joint_guidance_std = torch.std(joint_guidance, dim=(0,1))   # Std across batch and time
+        joint_guidance_max = torch.amax(joint_guidance, dim=(0,1))
+        joint_guidance_min = torch.amin(joint_guidance, dim=(0,1))
+
+        # print("\nJoint Guidance Statistics (UnNormalized):")
+        # if torch.any(joint_guidance_mean != 0):
+        #     print(f"Mean per joint: {joint_guidance_mean}")
+        # if torch.any(joint_guidance_std != 0):
+        #     print(f"Std per joint: {joint_guidance_std}")
+        # if torch.any(joint_guidance_max != 0):
+        #     print(f"Max per joint: {joint_guidance_max}")
+        # if torch.any(joint_guidance_min != 0):
+        #     print(f"Min per joint: {joint_guidance_min}")
+
+        joint_guidance_normalized = self.dataset.normalizer.normalize(joint_guidance, self.dataset.data_key_traj)
+
+        # print("\nJoint Guidance Statistics (Normalized):")
+        joint_guidance_norm_mean = torch.mean(joint_guidance_normalized, dim=(0,1))
+        joint_guidance_norm_std = torch.std(joint_guidance_normalized, dim=(0,1))
+        joint_guidance_norm_max = torch.amax(joint_guidance_normalized, dim=(0,1))
+        joint_guidance_norm_min = torch.amin(joint_guidance_normalized, dim=(0,1))
+
+        # if torch.any(joint_guidance_norm_mean != 0):
+        #     print(f"Mean per joint: {joint_guidance_norm_mean}")
+        # if torch.any(joint_guidance_norm_std != 0):
+        #     print(f"Std per joint: {joint_guidance_norm_std}")
+        # if torch.any(joint_guidance_norm_max != 0):
+        #     print(f"Max per joint: {joint_guidance_norm_max}")
+        # if torch.any(joint_guidance_norm_min != 0):
+        #     print(f"Min per joint: {joint_guidance_norm_min}")
+        
+        # end_time = time.time()
+        # print(f"GuideManagerJointStates.forward execution time: {end_time - start_time:.4f} seconds")
+        # return joint_guidance_normalized
+        return self.guidance_weight*joint_guidance
+
+    def _calculate_sdf_guidance(self, 
+                          sphere_poses_list: List[Dict[str, List[np.ndarray]]], 
+                          sphere_radii_list: List[Dict[str, List[float]]],
+                          sphere_jacobians_list: List[Dict[str, List[np.ndarray]]]):
+        batch_size = 1
+        
+        restructured_data = self._restructure_sphere_data(batch_size, sphere_poses_list, sphere_jacobians_list, sphere_radii_list)
+        
+        # calculate sdf distance
+        all_sphere_positions = restructured_data['sphere_poses']
+        # reshape to [batch, seq_len*spheres_per_timestep, 3]
+        all_sphere_positions = einops.rearrange(all_sphere_positions, 'b s n d -> b (s n) d')
+        
+        # Only set requires_grad for the positions tensor
+        all_sphere_positions.requires_grad_(True)
+        
+        # print("all_sphere_positions shape: ", all_sphere_positions.shape)
+        with torch.enable_grad():
+            sdf_values = self._sdf_model(all_sphere_positions) #[batch, seq_len*spheres_per_timestep]
+            # Reshape sdf_values back to [batch, seq_len, spheres_per_timestep]
+            # sdf_values = einops.rearrange(sdf_values, 'b (s n) -> b s n', 
+            #                      b=batch_size,  # batch size
+            #                      s=len(sphere_poses_list))  # sequence length
+            # print("sdf_values shape: ", sdf_values.shape)
+            
+            # Compute gradients only for the positions
+            sdf_values.sum().backward()
+            sdf_gradient = all_sphere_positions.grad  # Get gradient from positions tensor, shape: [batch, seq_len*spheres_per_timestep, 3]
+
+        
+
+        sdf_gradient = einops.rearrange(sdf_gradient, 'b (s n) d -> b s n d', 
+                                 b=batch_size,  # batch size
+                                 s=len(sphere_poses_list))  # sequence length
+        
+        sphere_mask = restructured_data['sphere_mask']
+        # print("sdf gradient shape: ", sdf_gradient.shape)
+        # print("sphere mask shape: ", sphere_mask.shape)
+        sdf_gradient = sdf_gradient * sphere_mask.unsqueeze(-1)
+
+        alpha = 2.5
+        norms = torch.norm(sdf_gradient, dim=-1, keepdim=True)
+        weights = torch.exp(-alpha * norms)
+        # print("weights shape: ", weights.shape)
+        # print("sdf_gradient shape: ", sdf_gradient.shape)
+        sdf_gradient_weighted = sdf_gradient * weights
+        sdf_gradient_weighted = torch.clamp(sdf_gradient_weighted, min=-0.1, max=0.1)
+        
+        # Debug feature: zero out gradients for sphere indices <= 10
+        # self.debug_only_spheres_gt_10 = True
+        # if hasattr(self, 'debug_only_spheres_gt_10') and self.debug_only_spheres_gt_10:
+        #     mask = torch.zeros_like(sdf_gradient_weighted)
+        #     mask[..., 19:, :] = 1  # keep only spheres with index > 10
+        #     sdf_gradient_weighted = sdf_gradient_weighted * mask
+        
+        self.debug_state.append({
+            'sdf_gradients': sdf_gradient_weighted,
+        })
+
+        # Only rearrange if not already 5D
+        if sdf_gradient_weighted.dim() == 4:
+            sdf_gradient_weighted = einops.rearrange(sdf_gradient_weighted, 'b t s d -> b t s 1 d')
+
+        joint_guidance = self._calculate_joint_guidance_from_sphere_sdf(restructured_data, sdf_gradient_weighted)
+        return joint_guidance
+
+    def _restructure_sphere_data(self, 
+                           batch_size: int,
+                           sphere_poses_list: List[Dict[str, List[np.ndarray]]],
+                           sphere_jacobians_list: List[Dict[str, List[np.ndarray]]],
+                           sphere_radii_list: List[Dict[str, List[float]]]) -> Dict[str, torch.Tensor]:
+        
+        seq_len = len(sphere_poses_list)
+        # Get unique link names and create mappings
+        link_names = list(sphere_poses_list[0].keys())
+
+        # Count total spheres per link across all timesteps
+        spheres_per_link = {link: len(spheres) for link, spheres in sphere_poses_list[0].items()}
+        total_spheres = sum(spheres_per_link.values())
+
+        # Create link to sphere mapping
+        # link_to_spheres = {}
+        # current_idx = 0
+        # for link, num_spheres in spheres_per_link.items():
+        #     link_to_spheres[link] = list(range(current_idx, current_idx + num_spheres))
+        #     current_idx += num_spheres
+
+        # Initialize tensors
+        sphere_poses = torch.zeros((batch_size, seq_len, total_spheres, 3), 
+                                device=self.device, dtype=self.dtype)
+        jacobians = torch.zeros((batch_size, seq_len, total_spheres, 6, sphere_jacobians_list[0][link_names[0]][0].shape[-1]), 
+                            device=self.device, dtype=self.dtype)
+        sphere_radii = torch.zeros((batch_size, seq_len, total_spheres),
+                                device=self.device, dtype=self.dtype)
+        sphere_mask = torch.zeros((batch_size, seq_len, total_spheres), 
+                                device=self.device, dtype=torch.bool)
+
+        # Fill tensors
+        for t in range(seq_len):
+            current_sphere_idx = 0
+            for link_name, spheres in sphere_poses_list[t].items():
+                if len(spheres) == 0:
+                    continue
+                    
+                # Convert positions to tensor
+                spheres_tensor = torch.from_numpy(np.stack(spheres)).to(
+                    device=self.device, dtype=self.dtype)
+                sphere_poses[0, t, current_sphere_idx:current_sphere_idx + len(spheres)] = spheres_tensor
+                
+                # Get corresponding jacobians
+                link_jacobians = sphere_jacobians_list[t][link_name]
+                jacobians[0, t, current_sphere_idx:current_sphere_idx + len(spheres)] = torch.from_numpy(
+                    np.stack(link_jacobians)
+                ).to(device=self.device, dtype=self.dtype)
+                
+                # Get corresponding radii
+                radii = sphere_radii_list[t][link_name]
+                sphere_radii[0, t, current_sphere_idx:current_sphere_idx + len(spheres)] = torch.tensor(
+                    radii, device=self.device, dtype=self.dtype)
+                
+                # Set mask to True if link is in selected_links
+                if hasattr(self, '_selected_links') and link_name in self._selected_links:
+                    sphere_mask[0, t, current_sphere_idx:current_sphere_idx + len(spheres)] = True
+                
+                current_sphere_idx += len(spheres)
+        
+        return {
+            'sphere_poses': sphere_poses,  # [batch, timestep, sphere_number, 3]
+            'jacobians': jacobians,        # [batch, timestep, sphere_number, 6, n_joints]
+            'sphere_radii': sphere_radii,  # [batch, timestep, sphere_number]
+            'sphere_mask': sphere_mask     # [batch, timestep, sphere_number]
+        }
+
+    def _calculate_joint_guidance_from_sphere_sdf(self, 
+                                            restructured_data: Dict[str, torch.Tensor],
+                                            sdf_gradient_weighted: torch.Tensor) -> torch.Tensor:
+        """
+        Calculate joint guidance from sphere SDF gradients using parallel computation.
+        
+        Returns:
+            Joint guidance tensor of shape [num_joints]
+        """
+        # Extract data
+        sphere_jacobians = restructured_data['jacobians']  #
+        sphere_mask = restructured_data['sphere_mask']  # 
+
+        position_jacobian = sphere_jacobians[:, :, :, :3, :]  # [batch, timestep, sphere_number, 3, 9]
+        # From [batch, timestep, sphere_number, 3] to [batch, timestep, sphere_number, 1, 3]
+
+        # Print statistics about sdf_gradients_unsq
+        # print("SDF gradients shape:", sdf_gradient_weighted.shape)
+        # print("SDF gradients mean (per component):", sdf_gradient_weighted.mean(dim=(0,1,2)))
+        # print("SDF gradients std (per component):", sdf_gradient_weighted.std(dim=(0,1,2)))
+        # print("SDF gradients min (per component):", sdf_gradient_weighted.amin(dim=(0,1,2)))
+        # print("SDF gradients max (per component):", sdf_gradient_weighted.amax(dim=(0,1,2)))
+
+        # The multiplication / mask stuff here needs tobe double checked, I don't think it is correct
+        # 2. Matrix multiply (no einops needed here)
+        joint_grads = torch.matmul(sdf_gradient_weighted, position_jacobian)  # [b, t, s, 1, j]
+        
+        # 3. Remove singleton (now [b, t, s, j])
+        joint_grads = einops.rearrange(joint_grads, 'b t s 1 j -> b t s j')
+
+        # a no mask version
+        # Sum over sphere_number (axis s) to get final joint deltas:
+        delta_joint_states = einops.reduce(joint_grads, 'b t s j -> b t j', 'sum')
+
+        delta_joint_states = torch.clamp(delta_joint_states, min=-0.1, max=0.1)
+
+        # # 4. Mask broadcast ([s, j] -> [1, 1, s, j])
+        # # TODO: jacobian mask
+        # mask_broadcast = einops.rearrange(mask, 's j -> 1 1 s j')
+        # joint_grads = joint_grads * mask_broadcast
+        
+        return delta_joint_states
+
+        
+        
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/helpers.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..abc58f23ece2f3846a939c18a8da75219376e999
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/helpers.py
@@ -0,0 +1,109 @@
+"""
+Adapted from https://github.com/joaoamcarvalho/mpd-public
+"""
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+#-----------------------------------------------------------------------------#
+#---------------------------- variance schedules -----------------------------#
+#-----------------------------------------------------------------------------#
+
+def linear_beta_schedule(n_diffusion_steps, beta_start=0.0001, beta_end=0.02):
+    return torch.linspace(beta_start, beta_end, n_diffusion_steps)
+
+
+def quadratic_beta_schedule(n_diffusion_steps, beta_start=0.0001, beta_end=0.02):
+    return torch.linspace(beta_start**0.5, beta_end**0.5, n_diffusion_steps) ** 2
+
+
+def sigmoid_beta_schedule(n_diffusion_steps, beta_start=0.0001, beta_end=0.02):
+    betas = torch.linspace(-6, 6, n_diffusion_steps)
+    return torch.sigmoid(betas) * (beta_end - beta_start) + beta_start
+
+
+def cosine_beta_schedule(n_diffusion_steps, s=0.008, a_min=0, a_max=0.999, dtype=torch.float32):
+    """
+    cosine schedule
+    as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
+    """
+    steps = n_diffusion_steps + 1
+    x = np.linspace(0, steps, steps)
+    alphas_cumprod = np.cos(((x / steps) + s) / (1 + s) * np.pi * 0.5) ** 2
+    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
+    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
+    betas_clipped = np.clip(betas, a_min=a_min, a_max=a_max)
+    return torch.tensor(betas_clipped, dtype=dtype)
+
+
+def exponential_beta_schedule(n_diffusion_steps, beta_start=1e-4, beta_end=1.0):
+    # exponential increasing noise from t=0 to t=T
+    x = torch.linspace(0, n_diffusion_steps, n_diffusion_steps)
+    beta_start = torch.tensor(beta_start)
+    beta_end = torch.tensor(beta_end)
+    a = 1 / n_diffusion_steps * torch.log(beta_end / beta_start)
+    betas = beta_start * torch.exp(a * x)
+    return torch.clamp(betas, max=beta_end)  # Ensure values don't exceed beta_end
+
+
+def constant_fraction_beta_schedule(n_diffusion_steps):
+    # exponential increasing noise from t=0 to t=T
+    x = torch.linspace(0, n_diffusion_steps, n_diffusion_steps)
+    return 1 / (n_diffusion_steps-x+1)
+
+
+def variance_preserving_beta_schedule(n_diffusion_steps, beta_start=1e-4, beta_end=1.0):
+    # Works only with a small number of diffusion steps
+    # https://arxiv.org/abs/2112.07804
+    # https://openreview.net/pdf?id=AHvFDPi-FA
+    x = torch.linspace(0, n_diffusion_steps, n_diffusion_steps)
+    alphas = torch.exp(-beta_start*(1/n_diffusion_steps) - 0.5*(beta_end-beta_start)*(2*x-1)/(n_diffusion_steps**2))
+    betas = 1 - alphas
+    return betas
+
+
+
+
+#-----------------------------------------------------------------------------#
+#---------------------------------- losses -----------------------------------#
+#-----------------------------------------------------------------------------#
+
+class WeightedLoss(nn.Module):
+
+    def __init__(self, weights=None):
+        super().__init__()
+        self.register_buffer('weights', weights)
+
+    def forward(self, pred, targ):
+        '''
+            pred, targ : tensor
+                [ batch_size x horizon x transition_dim ]
+        '''
+        loss = self._loss(pred, targ)
+        if self.weights is not None:
+            weighted_loss = (loss * self.weights).mean()
+        else:
+            weighted_loss = loss.mean()
+        return weighted_loss, {}
+
+
+class WeightedL1(WeightedLoss):
+
+    def _loss(self, pred, targ):
+        return torch.abs(pred - targ)
+
+
+class WeightedL2(WeightedLoss):
+
+    def _loss(self, pred, targ):
+        return F.mse_loss(pred, targ, reduction='none')
+
+
+Losses = {
+    'l1': WeightedL1,
+    'l2': WeightedL2,
+}
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/mlp_model.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/mlp_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b42834c4d37e8d336fd65b2f5f50c118042e8ddc
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/mlp_model.py
@@ -0,0 +1,56 @@
+from torch import nn
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops.layers.torch import Rearrange
+
+
+## Fully connected Neural Network block - Multi Layer Perceptron
+class MLP(nn.Module):
+    def __init__(self, in_dim, out_dim, hidden_dim=16, n_layers=1, act='relu', batch_norm=True):
+        super(MLP, self).__init__()
+        activations = {'relu': nn.ReLU, 'sigmoid': nn.Sigmoid, 'tanh': nn.Tanh, 'leaky_relu': nn.LeakyReLU,
+                       'elu': nn.ELU, 'prelu': nn.PReLU, 'softplus': nn.Softplus, 'mish': nn.Mish,
+                       'identity': nn.Identity
+                       }
+
+        act_func = activations[act]
+        layers = [nn.Linear(in_dim, hidden_dim), act_func()]
+        for i in range(n_layers):
+            layers += [
+                nn.Linear(hidden_dim, hidden_dim),
+                nn.BatchNorm1d(hidden_dim) if batch_norm else nn.Identity(),
+                act_func(),
+            ]
+        layers.append(nn.Linear(hidden_dim, out_dim))
+
+        self._network = nn.Sequential(
+            *layers
+        )
+
+    def forward(self, x):
+        return self._network(x)
+
+
+
+class MLPModel(nn.Module):
+    def __init__(self, in_dim=16, out_dim=16, input_field='x',
+                 output_field='y',
+                 pretrained_dir=None,
+                 **kwargs):
+        super().__init__()
+
+        self.input_field = input_field
+        self.output_field = output_field
+
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        self._net = MLP(in_dim, out_dim, **kwargs)
+
+    def forward(self, input):
+        x = input[self.input_field]
+        out = self._net(x)
+        return {self.output_field: out}
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/sample_functions.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/sample_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7b67fb289a7db12d7cace9a3f92920706527c68
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/sample_functions.py
@@ -0,0 +1,286 @@
+import torch
+from matplotlib import pyplot as plt
+import time
+from cfdp.diffusion_policy.utils.utils import to_numpy
+
+def apply_hard_conditioning(x, conditions):
+    for t, val in conditions.items():
+        # print("x: ", x.shape)
+        # print("val: ", val.shape)
+        x[:, t, :] = val.clone()
+    return x
+
+
+def extract(a, t, x_shape):
+    b, *_ = t.shape
+    out = a.gather(-1, t)
+    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+@torch.no_grad()
+def ddpm_sample_fn(
+        model, x, hard_conds, context, t,
+        guide=None,
+        n_guide_steps=1,
+        scale_grad_by_std=False,
+        t_start_guide=torch.inf,
+        noise_std_extra_schedule_fn=None,  # 'linear'
+        debug=False,
+        **kwargs
+):
+    t_single = t[0]
+    if t_single < 0:
+        t = torch.zeros_like(t)
+
+    model_mean, _, model_log_variance = model.p_mean_variance(x=x, hard_conds=hard_conds, context=context, t=t)
+    x = model_mean
+    # DEBUG Store the current state, timestep, and reconstruction todo: if debug
+    if debug:
+        if context is not None:
+                context_temp = model.context_model(context)
+        noise_temp = model.model(x, t, context_temp['condition'])
+        model.diffusion_history.append({
+            't': to_numpy(t),
+            'x': to_numpy(model_mean),
+            'noise': to_numpy(-1*noise_temp)
+        })
+
+    model_log_variance = extract(model.posterior_log_variance_clipped, t, x.shape)
+    model_std = torch.exp(0.5 * model_log_variance)
+    model_var = torch.exp(model_log_variance)
+
+    if guide is not None and t_single < t_start_guide:
+
+        x, grad_scaled_lst = guide_gradient_steps(
+            x,
+            hard_conds=hard_conds,
+            guide=guide,
+            n_guide_steps=n_guide_steps,
+            scale_grad_by_std=False,
+            model_var=model_var,
+        )
+        if debug:
+            model.grad_scaled_history.append({
+                't': to_numpy(t_single),
+                'x': to_numpy(model_mean),
+                'grad_scaled': to_numpy(grad_scaled_lst[0])
+            })
+    # no noise when t == 0
+    noise = torch.randn_like(x)
+    noise[t == 0] = 0
+
+    # For smoother results, we can decay the noise standard deviation throughout the diffusion
+    # this is roughly equivalent to using a temperature in the prior distribution
+    if noise_std_extra_schedule_fn is None:
+        noise_std = 1.0
+    else:
+        noise_std = noise_std_extra_schedule_fn(t_single)
+    values = None
+    return x + model_std * noise * noise_std, values
+
+
+@torch.no_grad()
+def ddpm_sample_fn_stomp(
+        model, x, hard_conds, context, t,
+        guide=None,
+        n_guide_steps=1,
+        scale_grad_by_std=False,
+        t_start_guide=torch.inf,
+        noise_std_extra_schedule_fn=None,  # 'linear'
+        debug=False,
+        **kwargs
+):
+    t_single = t[0]
+    if t_single < 0:
+        t = torch.zeros_like(t)
+
+    model_mean, _, model_log_variance = model.p_mean_variance(x=x, hard_conds=hard_conds, context=context, t=t)
+    x = model_mean
+    # DEBUG Store the current state, timestep, and reconstruction todo: if debug
+    if debug:
+        if context is not None:
+                context_temp = model.context_model(context)
+        noise_temp = model.model(x, t, context_temp['condition'])
+        model.diffusion_history.append({
+            't': to_numpy(t),
+            'x': to_numpy(model_mean),
+            'noise': to_numpy(-1*noise_temp)
+        })
+
+    model_log_variance = extract(model.posterior_log_variance_clipped, t, x.shape)
+    model_std = torch.exp(0.5 * model_log_variance)
+    model_var = torch.exp(model_log_variance)
+
+
+    # if guide is not None and t_single in [1, 0]:
+    #     k = min(2, x.shape[0])
+    #     # x = inject_noise_to_mean(x, std=0.08)
+    #     x = select_best_trajectories(x, guide, k)
+    #     # x = smooth_trajectory_batch(x, weight=0.99)
+    #     print("x.shape:", x.shape)
+    
+    if guide is not None and t_single < t_start_guide:
+        x, grad_scaled_lst = guide_gradient_steps(
+            x,
+            hard_conds=hard_conds,
+            guide=guide,
+            n_guide_steps=n_guide_steps,
+            scale_grad_by_std=scale_grad_by_std,
+            model_var=model_var,
+        )
+        
+        if debug:
+            model.grad_scaled_history.append({
+                't': to_numpy(t_single),
+                'x': to_numpy(model_mean),
+                'grad_scaled': to_numpy(grad_scaled_lst[0])
+            })
+            
+    # no noise when t == 0
+    noise = torch.randn_like(x)
+    noise[t == 0] = 0
+
+    # For smoother results, we can decay the noise standard deviation throughout the diffusion
+    # this is roughly equivalent to using a temperature in the prior distribution
+    if noise_std_extra_schedule_fn is None:
+        noise_std = 1.0
+    else:
+        noise_std = noise_std_extra_schedule_fn(t_single)
+    values = None
+
+    return x + model_std * noise * noise_std, values
+
+def select_best_trajectories(x, guide, k=2):
+    guide(x, get_cost_rank=True)  # [batch]
+    sorted_indices = guide.sdf_cost_sorted_indices[:k]  # [k]
+    x_selected = x[sorted_indices]  # [k, seq_len, joint_dim]
+    batch = x.shape[0]
+    if k == 0:
+        raise ValueError("No trajectories selected (k=0).")
+        
+    # Randomly sample k trajectories to create batch_size trajectories
+    indices = torch.randint(0, k, (batch,), device=x_selected.device)  # [batch]
+    x_new = x_selected[indices]  # [batch, seq_len, joint_dim]
+    return x_new
+
+def guide_with_selection(x,
+    hard_conds=None,
+    guide=None,
+    n_guide_steps=1, scale_grad_by_std=False,
+    model_var=None,
+    k=2,
+    **kwargs
+):
+    grad_scaled_lst = []
+    x_new = select_best_trajectories(x, guide, k)
+
+    # Apply gradient steps
+    for step in range(n_guide_steps):
+        grad_scaled = guide(x_new, get_cost_rank=True)
+        if scale_grad_by_std:
+            grad_scaled = model_var * grad_scaled
+        x = x + grad_scaled
+        x = apply_hard_conditioning(x, hard_conds)
+        grad_scaled_lst.append(grad_scaled)
+    
+    return x, grad_scaled_lst
+
+def guide_gradient_steps(
+    x,
+    hard_conds=None,
+    guide=None,
+    n_guide_steps=1, scale_grad_by_std=False,
+    model_var=None,
+    **kwargs
+):
+    grad_scaled_lst = []
+    for step in range(n_guide_steps):
+        grad_scaled = guide(x)
+        # print("grad_scaled: ", grad_scaled)
+        if scale_grad_by_std:
+            grad_scaled = model_var * grad_scaled
+        # print("grad_scaled: ", grad_scaled)
+        x = x + grad_scaled
+        x = apply_hard_conditioning(x, hard_conds)
+        grad_scaled_lst.append(grad_scaled)
+    
+    return x, grad_scaled_lst
+
+def smooth_trajectory_batch(traj: torch.Tensor, weight: float = 0.4) -> torch.Tensor:
+    traj_smooth = traj.clone()
+    # Smooth all inner waypoints by blending with neighbors
+    traj_smooth[:, 1:-1, :] = (
+        (1 - weight) * traj[:, 1:-1, :]
+        + (weight / 2) * (traj[:, :-2, :] + traj[:, 2:, :])
+    )
+    return traj_smooth
+
+def inject_stomp_noise(x: torch.Tensor, num_samples: int = 2, std: float = 0.1) -> torch.Tensor:
+    """
+    Randomly select a subset of trajectories from x and add Gaussian noise (STOMP-style).
+    Args:
+        x: torch.Tensor of shape [batch, seq_len, joint_dim] or similar
+            The batch of trajectories or states to be perturbed.
+        num_samples: int, optional (default=2)
+            Number of trajectories to add noise to.
+        std: float, optional (default=0.1)
+            Standard deviation of the Gaussian noise to be added.
+    Returns:
+        x_noisy: torch.Tensor of the same shape as x,
+            with selected trajectories perturbed.
+        selected_indices: torch.Tensor containing the indices of the perturbed trajectories.
+    """
+    x_noisy = x.clone()
+    batch_size, seq_len, joint_dim = x.shape
+    device = x.device
+    # Select random indices
+    selected_indices = torch.randperm(batch_size, device=device)[:num_samples]
+
+    # Create time weights: [seq_len, 1], e.g., sin(pi * t)
+    t = torch.linspace(0, 1, seq_len, device=device)
+    time_weights = torch.sin(t * torch.pi).unsqueeze(1)  # shape: [seq_len, 1]
+
+    # Generate Gaussian noise and apply time-based weights
+    noise = torch.randn((num_samples, seq_len, joint_dim), device=device) * std
+    noise *= time_weights  # Broadcast [seq_len, 1] to [num_samples, seq_len, joint_dim]
+
+    # Inject into selected trajectories
+    x_noisy[selected_indices] += noise
+
+    return x_noisy, selected_indices
+
+def inject_noise_to_mean(x: torch.Tensor, std: float = 0.1) -> torch.Tensor:
+    """
+    Compute mean trajectory and add noise to copies of it.
+    Args:
+        x: torch.Tensor of shape [batch, seq_len, joint_dim] or similar
+            The batch of trajectories or states.
+        std: float, optional (default=0.1)
+            Standard deviation of the Gaussian noise to be added.
+    Returns:
+        x_noisy: torch.Tensor of the same shape as x,
+            with noisy copies of the mean trajectory.
+    """
+    batch_size, seq_len, joint_dim = x.shape
+    device = x.device
+
+    # Compute mean trajectory across batch dimension
+    mean_traj = x.mean(dim=0, keepdim=True)  # [1, seq_len, joint_dim]
+    
+    # Expand mean trajectory to match batch size
+    mean_traj = mean_traj.expand(batch_size, -1, -1)  # [batch, seq_len, joint_dim]
+
+    # Create time weights: [seq_len, 1], e.g., sin(pi * t)
+    t = torch.linspace(0, 1, seq_len, device=device)
+    time_weights = torch.sin(t * torch.pi).unsqueeze(1)  # shape: [seq_len, 1]
+    print("time_weights:", time_weights)
+    
+
+    # Generate Gaussian noise and apply time-based weights
+    noise = torch.randn((batch_size, seq_len, joint_dim), device=device) * std
+    noise *= time_weights  # Broadcast [seq_len, 1] to [batch_size, seq_len, joint_dim]
+
+    # Add noise to the mean trajectory copies
+    x_noisy = mean_traj + noise
+
+    return x_noisy
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/temporal_unet.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/temporal_unet.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0cea9e52c743ed4874b5f6124c938608bafd96f
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/models/temporal_unet.py
@@ -0,0 +1,370 @@
+"""
+Adapted from https://github.com/joaoamcarvalho/mpd-public
+"""
+import einops
+import numpy as np
+import torch
+import torch.nn as nn
+from abc import ABC
+from einops import rearrange
+from torch.nn import DataParallel
+
+from cfdp.diffusion_policy.layers.layers import Downsample1d, Conv1dBlock, Upsample1d, \
+    ResidualTemporalBlock, FiLMResidualTemporalBlock, TimeEncoder, MLP, group_norm_n_groups, LinearAttention, PreNorm, Residual, TemporalBlockMLP
+from cfdp.diffusion_policy.layers.layers_attention import SpatialTransformer
+
+
+UNET_DIM_MULTS = {
+    0: (1, 2, 4),
+    1: (1, 2, 4, 8),
+    2: (1, 2, 4, 8, 16)
+}
+
+class TemporalUnet(nn.Module):
+
+    def __init__(
+            self,
+            n_support_points=None,
+            state_dim=None,
+            unet_input_dim=32,
+            dim_mults=(1, 2, 4, 8),
+            time_emb_dim=32,
+            self_attention=False,
+            conditioning_embed_dim=4,
+            conditioning_type=None,
+            attention_num_heads=2,
+            attention_dim_head=32,
+            **kwargs
+    ):
+        super().__init__()
+
+        self.state_dim = state_dim
+        input_dim = state_dim
+
+        # Conditioning
+        if conditioning_type is None or conditioning_type == 'None':
+            conditioning_type = None
+        elif conditioning_type == 'concatenate':
+            if self.state_dim < conditioning_embed_dim // 4:
+                # Embed the state in a latent space HxF if the conditioning embedding is much larger than the state
+                state_emb_dim = conditioning_embed_dim // 4
+                self.state_encoder = MLP(state_dim, state_emb_dim, hidden_dim=state_emb_dim//2, n_layers=1, act='mish')
+            else:
+                state_emb_dim = state_dim
+                self.state_encoder = nn.Identity()
+            input_dim = state_emb_dim + conditioning_embed_dim
+        elif conditioning_type == 'attention':
+            pass
+        elif conditioning_type == 'default':
+            pass
+        else:
+            raise NotImplementedError
+        self.conditioning_type = conditioning_type
+
+        dims = [input_dim, *map(lambda m: unet_input_dim * m, dim_mults)]
+        in_out = list(zip(dims[:-1], dims[1:]))
+        print(f'[ models/temporal ] Channel dimensions: {in_out}')
+
+        # Networks
+        self.time_mlp = TimeEncoder(32, time_emb_dim)
+
+        # conditioning dimension (time + context)
+        cond_dim = time_emb_dim + (conditioning_embed_dim if conditioning_type == 'default' else 0)
+
+        # Unet
+        self.downs = nn.ModuleList([])
+        self.ups = nn.ModuleList([])
+        num_resolutions = len(in_out)
+
+        for ind, (dim_in, dim_out) in enumerate(in_out):
+            is_last = ind >= (num_resolutions - 1)
+
+            self.downs.append(nn.ModuleList([
+                FiLMResidualTemporalBlock(dim_in, dim_out, cond_dim, n_support_points=n_support_points),
+                FiLMResidualTemporalBlock(dim_out, dim_out, cond_dim, n_support_points=n_support_points),
+                Residual(PreNorm(dim_out, LinearAttention(dim_out))) if self_attention else nn.Identity(),
+                SpatialTransformer(dim_out, attention_num_heads, attention_dim_head, depth=1,
+                                   context_dim=conditioning_embed_dim) if conditioning_type == 'attention' else None,
+                Downsample1d(dim_out) if not is_last else nn.Identity()
+            ]))
+
+            if not is_last:
+                n_support_points = n_support_points // 2
+
+        mid_dim = dims[-1]
+        self.mid_block1 = FiLMResidualTemporalBlock(mid_dim, mid_dim, cond_dim, n_support_points=n_support_points)
+        self.mid_attn = Residual(PreNorm(mid_dim, LinearAttention(mid_dim))) if self_attention else nn.Identity()
+        self.mid_attention = SpatialTransformer(mid_dim, attention_num_heads, attention_dim_head, depth=1,
+                                                context_dim=conditioning_embed_dim) if conditioning_type == 'attention' else nn.Identity()
+        self.mid_block2 = ResidualTemporalBlock(mid_dim, mid_dim, cond_dim, n_support_points=n_support_points)
+
+        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
+            is_last = ind >= (num_resolutions - 1)
+
+            self.ups.append(nn.ModuleList([
+                FiLMResidualTemporalBlock(dim_out * 2, dim_in, cond_dim, n_support_points=n_support_points),
+                FiLMResidualTemporalBlock(dim_in, dim_in, cond_dim, n_support_points=n_support_points),
+                Residual(PreNorm(dim_in, LinearAttention(dim_in))) if self_attention else nn.Identity(),
+                SpatialTransformer(dim_in, attention_num_heads, attention_dim_head, depth=1,
+                                   context_dim=conditioning_embed_dim) if conditioning_type == 'attention' else None,
+                Upsample1d(dim_in) if not is_last else nn.Identity()
+            ]))
+
+            if not is_last:
+                n_support_points = n_support_points * 2
+
+        self.final_conv = nn.Sequential(
+            Conv1dBlock(unet_input_dim, unet_input_dim, kernel_size=5, n_groups=group_norm_n_groups(unet_input_dim)),
+            nn.Conv1d(unet_input_dim, state_dim, 1),
+        )
+
+    def forward(self, x, time, context):
+        """
+        x : [ batch x horizon x state_dim ]
+        context: [batch x context_dim]
+        """
+        b, h, d = x.shape
+
+        t_emb = self.time_mlp(time)
+        c_emb = t_emb
+        
+        if self.conditioning_type == 'concatenate':
+            x_emb = self.state_encoder(x)
+            context = einops.repeat(context, 'm n -> m h n', h=h)
+            x = torch.cat((x_emb, context), dim=-1)
+        elif self.conditioning_type == 'attention':
+            # reshape to keep the interface
+            context = einops.rearrange(context, 'b d -> b 1 d')
+        elif self.conditioning_type == 'default':
+            # Ensure context has the same shape as t_emb for concatenation
+            c_emb = torch.cat((t_emb, context), dim=-1)
+
+
+        # swap horizon and channels (state_dim)
+        x = einops.rearrange(x, 'b h c -> b c h')  # batch, horizon, channels (state_dim)
+
+        h = []
+        for resnet, resnet2, attn_self, attn_conditioning, downsample in self.downs:
+            x = resnet(x, c_emb)
+            # if self.conditioning_type == 'attention':
+            #     x = attention1(x, context=conditioning_emb)
+            x = resnet2(x, c_emb)
+            x = attn_self(x)
+            if self.conditioning_type == 'attention':
+                x = attn_conditioning(x, context=context)
+            h.append(x)
+            x = downsample(x)
+
+        x = self.mid_block1(x, c_emb)
+        x = self.mid_attn(x)
+        if self.conditioning_type == 'attention':
+            x = self.mid_attention(x, context=context)
+        x = self.mid_block2(x, c_emb)
+
+        for resnet, resnet2, attn_self, attn_conditioning, upsample in self.ups:
+            x = torch.cat((x, h.pop()), dim=1)
+            x = resnet(x, c_emb)
+            x = resnet2(x, c_emb)
+            x = attn_self(x)
+            if self.conditioning_type == 'attention':
+                x = attn_conditioning(x, context=context)
+            x = upsample(x)
+
+        x = self.final_conv(x)
+
+        x = einops.rearrange(x, 'b c h -> b h c')
+
+        return x
+
+
+class EnvModel(nn.Module):
+
+    def __init__(
+            self,
+            in_dim=16,
+            out_dim=16,
+            **kwargs
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        self.net = nn.Identity()
+
+    def forward(self, input_d):
+        env = input_d['env']
+        env_emb = self.net(env)
+        return env_emb
+
+
+class TaskModel(nn.Module):
+
+    def __init__(
+            self,
+            in_dim=16,
+            out_dim=32,
+            **kwargs
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        self.net = nn.Identity()
+
+    def forward(self, input_d):
+        task = input_d['tasks']
+        task_emb = self.net(task)
+        return task_emb
+
+
+class TaskModelNew(nn.Module):
+
+    def __init__(
+            self,
+            in_dim=16,
+            out_dim=32,
+            **kwargs
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        self.net = nn.Identity()
+
+    def forward(self, task):
+        task_emb = self.net(task)
+        return task_emb
+
+
+class ContextModel(nn.Module):
+
+    def __init__(
+            self,
+            env_model=None,
+            task_model=None,
+            out_dim=32,
+            **kwargs
+    ):
+        super().__init__()
+
+        self.env_model = env_model
+        self.task_model = task_model
+
+        self.in_dim = self.env_model.out_dim + self.task_model.out_dim
+
+        # self.out_dim = out_dim
+        # self.net = MLP(self.in_dim, self.out_dim, hidden_dim=out_dim, n_layers=1, act='mish')
+
+        self.out_dim = self.in_dim
+        self.net = nn.Identity()
+
+    def forward(self, input_d=None):
+        if input_d is None:
+            return None
+        env_emb = self.env_model(input_d)
+        task_emb = self.task_model(input_d)
+        context = torch.cat((env_emb, task_emb), dim=-1)
+        context_emb = self.net(context)
+        return context_emb
+
+
+class PointUnet(nn.Module):
+
+    def __init__(
+            self,
+            n_support_points=None,
+            state_dim=None,
+            dim=32,
+            dim_mults=(1, 2, 4),
+            time_emb_dim=32,
+            conditioning_embed_dim=4,
+            conditioning_type=None,
+            **kwargs
+    ):
+        super().__init__()
+
+        self.dim_mults = dim_mults
+
+        self.state_dim = state_dim
+        input_dim = state_dim
+
+        # Conditioning
+        if conditioning_type is None or conditioning_type == 'None':
+            conditioning_type = None
+        elif conditioning_type == 'concatenate':
+            if self.state_dim < conditioning_embed_dim // 4:
+                # Embed the state in a latent space HxF if the conditioning embedding is much larger than the state
+                state_emb_dim = conditioning_embed_dim // 4
+                self.state_encoder = MLP(state_dim, state_emb_dim, hidden_dim=state_emb_dim//2, n_layers=1, act='mish')
+            else:
+                state_emb_dim = state_dim
+                self.state_encoder = nn.Identity()
+            input_dim = state_emb_dim + conditioning_embed_dim
+        elif conditioning_type == 'default':
+            pass
+        else:
+            raise NotImplementedError
+        self.conditioning_type = conditioning_type
+
+        dims = [input_dim, *map(lambda m: dim * m, self.dim_mults)]
+        in_out = list(zip(dims[:-1], dims[1:]))
+        print(f'[ models/temporal ] Channel dimensions: {in_out}')
+
+        # Networks
+        self.time_mlp = TimeEncoder(32, time_emb_dim)
+
+        # conditioning dimension (time + context)
+        cond_dim = time_emb_dim + (conditioning_embed_dim if conditioning_type == 'default' else 0)
+
+        # Unet
+        self.downs = nn.ModuleList([])
+        self.ups = nn.ModuleList([])
+
+        for ind, (dim_in, dim_out) in enumerate(in_out):
+            self.downs.append(nn.ModuleList([
+                TemporalBlockMLP(dim_in, dim_out, cond_dim)
+            ]))
+
+        mid_dim = dims[-1]
+        self.mid_block1 = TemporalBlockMLP(mid_dim, mid_dim, cond_dim)
+
+        for ind, (dim_in, dim_out) in enumerate(reversed(in_out[1:])):
+            self.ups.append(nn.ModuleList([
+                TemporalBlockMLP(dim_out * 2, dim_in, cond_dim)
+            ]))
+
+        self.final_layer = nn.Sequential(
+            MLP(dim, state_dim, hidden_dim=dim, n_layers=0, act='identity')
+        )
+
+    def forward(self, x, time, context):
+        """
+        x : [ batch x horizon x state_dim ]
+        context: [batch x context_dim]
+        """
+        x = einops.rearrange(x, 'b 1 d -> b d')
+
+        t_emb = self.time_mlp(time)
+        c_emb = t_emb
+        if self.conditioning_type == 'concatenate':
+            x_emb = self.state_encoder(x)
+            x = torch.cat((x_emb, context), dim=-1)
+        elif self.conditioning_type == 'default':
+            c_emb = torch.cat((t_emb, context), dim=-1)
+
+        h = []
+        for resnet, in self.downs:
+            x = resnet(x, c_emb)
+            h.append(x)
+
+        x = self.mid_block1(x, c_emb)
+
+        for resnet, in self.ups:
+            x = torch.cat((x, h.pop()), dim=1)
+            x = resnet(x, c_emb)
+
+        x = self.final_layer(x)
+
+        x = einops.rearrange(x, 'b d -> b 1 d')
+
+        return x
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..498ff8a9c1008123d76f089cd03abc1319f9b1e8
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/__init__.py
@@ -0,0 +1 @@
+from .trainer import train, get_num_epochs
\ No newline at end of file
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/trainer.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d1e503e8197bb85a0d08790845196fd56b450a9
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/trainer/trainer.py
@@ -0,0 +1,385 @@
+import copy
+from math import ceil
+
+import numpy as np
+import os
+import time
+import collections
+import torch
+import wandb
+from collections import defaultdict
+from tqdm.autonotebook import tqdm
+from cfdp.diffusion_policy.utils.utils import TimerCUDA
+
+
+#TODO: select which GPU
+DEFAULT_TENSOR_ARGS = {'device': 'cuda', 'dtype': torch.float32}
+
+def get_torch_device(device='cuda'):
+    if 'cuda' in device and torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    return torch.device(device)
+
+
+def dict_to_device(ob, device):
+    if isinstance(ob, (dict, collections.abc.Mapping)):
+        return {k: dict_to_device(v, device) for k, v in ob.items()}
+    else:
+        return ob.to(device)
+
+
+def to_numpy(x, dtype=np.float32, clone=False):
+    if torch.is_tensor(x):
+        x = x.detach().cpu().numpy().astype(dtype)
+        return x
+    if isinstance(x, np.ndarray):
+        return x.astype(dtype, copy=clone)
+    return np.array(x).astype(dtype)
+
+
+def get_num_epochs(num_train_steps, batch_size, dataset_len):
+    return ceil(num_train_steps * batch_size / dataset_len)
+
+
+def save_models_to_disk(models_prefix_l, epoch, total_steps, checkpoints_dir=None):
+    for model, prefix in models_prefix_l:
+        if model is not None:
+            save_model_to_disk(model, epoch, total_steps, checkpoints_dir, prefix=f'{prefix}_')
+            for submodule_key, submodule_value in model.submodules.items():
+                save_model_to_disk(submodule_value, epoch, total_steps, checkpoints_dir,
+                                   prefix=f'{prefix}_{submodule_key}_')
+
+
+def save_model_to_disk(model, epoch, total_steps, checkpoints_dir=None, prefix='model_'):
+    # If the model is frozen we do not save it again, since the parameters did not change
+    if hasattr(model, 'is_frozen') and model.is_frozen:
+        return
+
+    torch.save(model.state_dict(), os.path.join(checkpoints_dir, f'{prefix}current_state_dict.pth'))
+    torch.save(model.state_dict(), os.path.join(checkpoints_dir, f'{prefix}epoch_{epoch:04d}_iter_{total_steps:06d}_state_dict.pth'))
+    torch.save(model, os.path.join(checkpoints_dir, f'{prefix}current.pth'))
+    torch.save(model, os.path.join(checkpoints_dir, f'{prefix}epoch_{epoch:04d}_iter_{total_steps:06d}.pth'))
+
+def save_losses_to_disk(train_losses, val_losses, checkpoints_dir=None):
+    np.save(os.path.join(checkpoints_dir, f'train_losses.npy'), train_losses)
+    np.save(os.path.join(checkpoints_dir, f'val_losses.npy'), val_losses)
+
+
+class EarlyStopper:
+    # https://stackoverflow.com/questions/71998978/early-stopping-in-pytorch
+
+    def __init__(self, patience=10, min_delta=0):
+        self.patience = patience  # use -1 to deactivate it
+        self.min_delta = min_delta
+        self.counter = 0
+        self.min_validation_loss = torch.inf
+
+    def early_stop(self, validation_loss):
+        if self.patience == -1:
+            return
+        if validation_loss < self.min_validation_loss:
+            self.min_validation_loss = validation_loss
+            self.counter = 0
+        elif validation_loss > (self.min_validation_loss + self.min_delta):
+            self.counter += 1
+            if self.counter >= self.patience:
+                return True
+        return False
+
+
+class EMA:
+    """
+    https://github.com/jannerm/diffuser
+    (empirical) exponential moving average parameters
+    """
+
+    def __init__(self, beta=0.995):
+        super().__init__()
+        self.beta = beta
+
+    def update_model_average(self, ema_model, current_model):
+        for ema_params, current_params in zip(ema_model.parameters(), current_model.parameters()):
+            old_weight, up_weight = ema_params.data, current_params.data
+            ema_params.data = self.update_average(old_weight, up_weight)
+
+    def update_average(self, old, new):
+        if old is None:
+            return new
+        return old * self.beta + (1 - self.beta) * new
+
+
+def do_summary(
+        summary_fn,
+        train_steps_current,
+        model,
+        batch_dict,
+        loss_info,
+        datasubset,
+        **kwargs
+):
+    if summary_fn is None:
+        return
+
+    with torch.no_grad():
+        # set model to evaluation mode
+        model.eval()
+
+        summary_fn(train_steps_current,
+                   model,
+                   batch_dict=batch_dict,
+                   loss_info=loss_info,
+                   datasubset=datasubset,
+                   **kwargs
+                   )
+
+    # set model to training mode
+    model.train()
+
+
+def train(model=None, train_dataloader=None, epochs=None, lr=None, steps_til_summary=None, model_dir=None, loss_fn=None,
+          train_subset=None,
+          summary_fn=None, steps_til_checkpoint=None,
+          val_dataloader=None, val_subset=None,
+          clip_grad=False,
+          clip_grad_max_norm=1.0,
+          val_loss_fn=None,
+          optimizers=None, steps_per_validation=10, max_steps=None,
+          use_ema: bool = True,
+          ema_decay: float = 0.995, step_start_ema: int = 1000, update_ema_every: int = 10,
+          use_amp=False,
+          early_stopper_patience=-1,
+          debug=False,
+          tensor_args=DEFAULT_TENSOR_ARGS,
+          **kwargs
+          ):
+
+    print(f'\n------- TRAINING STARTED -------\n')
+
+    ema_model = None
+    if use_ema:
+        # Exponential moving average model
+        ema = EMA(beta=ema_decay)
+        ema_model = copy.deepcopy(model)
+
+    # Model optimizers
+    if optimizers is None:
+        optimizers = [torch.optim.Adam(lr=lr, params=model.parameters())]
+
+    # Automatic Mixed Precision
+    scaler = torch.amp.GradScaler('cuda', enabled=use_amp)
+
+    if val_dataloader is not None:
+        assert val_loss_fn is not None, "If validation set is passed, have to pass a validation loss_fn!"
+
+    ## Build saving directories
+    os.makedirs(model_dir, exist_ok=True)
+
+    summaries_dir = os.path.join(model_dir, 'summaries')
+    os.makedirs(summaries_dir, exist_ok=True)
+
+    checkpoints_dir = os.path.join(model_dir, 'checkpoints')
+    os.makedirs(checkpoints_dir, exist_ok=True)
+
+    # Early stopping
+    early_stopper = EarlyStopper(patience=early_stopper_patience, min_delta=0)
+
+    stop_training = False
+    train_steps_current = 0
+
+    # save models before training
+    save_models_to_disk([(model, 'model'), (ema_model, 'ema_model')], 0, 0, checkpoints_dir)
+
+    with tqdm(total=len(train_dataloader) * epochs, mininterval=1 if debug else 60) as pbar:
+        train_losses_l = []
+        validation_losses_l = []
+        for epoch in range(epochs):
+            model.train()  # set model to training mode
+            for step, train_batch_dict in enumerate(train_dataloader):
+                ####################################################################################################
+                # TRAINING LOSS
+                ####################################################################################################
+                with TimerCUDA() as t_training_loss:
+                    train_batch_dict = dict_to_device(train_batch_dict, tensor_args['device'])
+
+                    # Compute losses
+                    with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=use_amp):
+                        train_losses, train_losses_info = loss_fn(model, train_batch_dict, train_subset.dataset)
+
+                    train_loss_batch = 0.
+                    train_losses_log = {}
+                    for loss_name, loss in train_losses.items():
+                        single_loss = loss.mean()
+                        train_loss_batch += single_loss
+                        train_losses_log[loss_name] = to_numpy(single_loss).item()
+
+                ####################################################################################################
+                # SUMMARY
+                if train_steps_current % steps_til_summary == 0:
+                    # TRAINING
+                    print(f"\n-----------------------------------------")
+                    print(f"train_steps_current: {train_steps_current}")
+                    print(f"t_training_loss: {t_training_loss.elapsed:.4f} sec")
+                    print(f"Total training loss {train_loss_batch:.4f}")
+                    print(f"Training losses {train_losses}")
+
+                    train_losses_l.append((train_steps_current, train_losses_log))
+
+                    with TimerCUDA() as t_training_summary:
+                        do_summary(
+                            summary_fn,
+                            train_steps_current,
+                            ema_model if ema_model is not None else model,
+                            train_batch_dict,
+                            train_losses_info,
+                            train_subset,
+                            prefix='TRAINING ',
+                            debug=debug,
+                            tensor_args=tensor_args
+                        )
+                    print(f"t_training_summary: {t_training_summary.elapsed:.4f} sec")
+
+                    ################################################################################################
+                    # VALIDATION LOSS and SUMMARY
+                    validation_losses_log = {}
+                    if val_dataloader is not None:
+
+                        #Remove time cuda
+                        print("Running validation...")
+                        val_losses = defaultdict(list)
+                        total_val_loss = 0.
+                        for step_val, batch_dict_val in enumerate(val_dataloader):
+                            batch_dict_val = dict_to_device(batch_dict_val, tensor_args['device'])
+                            val_loss, val_loss_info = loss_fn(
+                                model, batch_dict_val, val_subset.dataset, step=train_steps_current)
+                            for name, value in val_loss.items():
+                                single_loss = to_numpy(value)
+                                val_losses[name].append(single_loss)
+                                total_val_loss += np.mean(single_loss).item()
+
+                            if step_val == steps_per_validation:
+                                break
+
+                        validation_losses = {}
+                        for loss_name, loss in val_losses.items():
+                            single_loss = np.mean(loss).item()
+                            validation_losses[f'VALIDATION {loss_name}'] = single_loss
+                        print(f"Validation losses {validation_losses}")
+
+                        # with TimerCUDA() as t_validation_loss:
+                        #     print("Running validation...")
+                        #     val_losses = defaultdict(list)
+                        #     total_val_loss = 0.
+                        #     for step_val, batch_dict_val in enumerate(val_dataloader):
+                        #         batch_dict_val = dict_to_device(batch_dict_val, tensor_args['device'])
+                        #         val_loss, val_loss_info = loss_fn(
+                        #             model, batch_dict_val, val_subset.dataset, step=train_steps_current)
+                        #         for name, value in val_loss.items():
+                        #             single_loss = to_numpy(value)
+                        #             val_losses[name].append(single_loss)
+                        #             total_val_loss += np.mean(single_loss).item()
+
+                        #         if step_val == steps_per_validation:
+                        #             break
+
+                        #     validation_losses = {}
+                        #     for loss_name, loss in val_losses.items():
+                        #         single_loss = np.mean(loss).item()
+                        #         validation_losses[f'VALIDATION {loss_name}'] = single_loss
+                        #     print("... finished validation.")
+
+                        # print(f"t_validation_loss: {t_validation_loss.elapsed:.4f} sec")
+                        # print(f"Validation losses {validation_losses}")
+
+                        validation_losses_log = validation_losses
+                        validation_losses_l.append((train_steps_current, validation_losses_log))
+
+                        # The validation summary is done only on one batch of the validation data
+                        with TimerCUDA() as t_validation_summary:
+                            do_summary(
+                                summary_fn,
+                                train_steps_current,
+                                ema_model if ema_model is not None else model,
+                                batch_dict_val,
+                                val_loss_info,
+                                val_subset,
+                                prefix='VALIDATION ',
+                                debug=debug,
+                                tensor_args=tensor_args
+                            )
+                        print(f"t_valididation_summary: {t_validation_summary.elapsed:.4f} sec")
+
+                    wandb.log({**train_losses_log, **validation_losses_log}, step=train_steps_current)
+
+                ####################################################################################################
+                # Early stopping
+                if early_stopper.early_stop(total_val_loss):
+                    print(f'Early stopped training at {train_steps_current} steps.')
+                    stop_training = True
+
+                ####################################################################################################
+                # OPTIMIZE TRAIN LOSS BATCH
+                with TimerCUDA() as t_training_optimization:
+                    for optim in optimizers:
+                        optim.zero_grad()
+
+                    scaler.scale(train_loss_batch).backward()
+
+                    if clip_grad:
+                        for optim in optimizers:
+                            scaler.unscale_(optim)
+                        torch.nn.utils.clip_grad_norm_(
+                            model.parameters(),
+                            max_norm=clip_grad_max_norm if isinstance(clip_grad, bool) else clip_grad
+                        )
+
+                    for optim in optimizers:
+                        scaler.step(optim)
+
+                    scaler.update()
+
+                    if ema_model is not None:
+                        if train_steps_current % update_ema_every == 0:
+                            # update ema
+                            if train_steps_current < step_start_ema:
+                                # reset parameters ema
+                                ema_model.load_state_dict(model.state_dict())
+                            ema.update_model_average(ema_model, model)
+
+                if train_steps_current % steps_til_summary == 0:
+                    print(f"t_training_optimization: {t_training_optimization.elapsed:.4f} sec")
+
+                ####################################################################################################
+                # SAVING
+                ####################################################################################################
+                pbar.update(1)
+                train_steps_current += 1
+
+                if (steps_til_checkpoint is not None) and (train_steps_current % steps_til_checkpoint == 0):
+                    save_models_to_disk([(model, 'model'), (ema_model, 'ema_model')],
+                                        epoch, train_steps_current, checkpoints_dir)
+                    save_losses_to_disk(train_losses_l, validation_losses_l, checkpoints_dir)
+
+                if stop_training or (max_steps is not None and train_steps_current == max_steps):
+                    break
+
+            if max_steps is not None and train_steps_current == max_steps:
+                break
+
+        # Update ema model at the end of training
+        if ema_model is not None:
+            # update ema
+            if train_steps_current < step_start_ema:
+                # reset parameters ema
+                ema_model.load_state_dict(model.state_dict())
+            ema.update_model_average(ema_model, model)
+
+        # Save model at end of training
+        save_models_to_disk([(model, 'model'), (ema_model, 'ema_model')],
+                            epoch, train_steps_current, checkpoints_dir)
+        save_losses_to_disk(train_losses_l, validation_losses_l, checkpoints_dir)
+
+        print(f'\n------- TRAINING FINISHED -------')
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9a21c820731a428dee71e740f33d3b24cd24398
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/__init__.py
@@ -0,0 +1 @@
+from .utils import TimerCUDA
\ No newline at end of file
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/loaders.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..627ec68343e9981a01d812904b6e73c5f6142767
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/loaders.py
@@ -0,0 +1,76 @@
+import os
+import torch
+from torch.utils.data import DataLoader, random_split
+from cfdp import datasets
+from cfdp.diffusion_policy import models
+from cfdp.diffusion_policy.utils import summary_trajectory
+from hydra.utils import instantiate
+
+def get_dataset(cfg_dataset,
+                batch_size=2,
+                val_set_size=0.05,
+                results_dir=None,
+                save_indices=False,
+                **kwargs):
+    print('\n---------------Loading dataset---------------')
+    print(f"cfg_dataset: {cfg_dataset}")
+    full_dataset = instantiate(cfg_dataset)
+    print(f" Dataset type: {type(full_dataset)}")
+
+    # split into train and validation
+    train_subset, val_subset = random_split(full_dataset, [1-val_set_size, val_set_size])
+    print(f"length of full dataset: {len(full_dataset)}")
+    print(f"length of train subset: {len(train_subset)}")
+    print(f"length of val subset: {len(val_subset)}")
+    train_dataloader = DataLoader(train_subset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=8)
+    val_dataloader = DataLoader(val_subset, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=8)
+
+    if save_indices:
+        # save the indices of training and validation sets (for later evaluation)
+        torch.save(train_subset.indices, os.path.join(results_dir, f'train_subset_indices.pt'))
+        torch.save(val_subset.indices, os.path.join(results_dir, f'val_subset_indices.pt'))
+
+    return train_subset, train_dataloader, val_subset, val_dataloader
+
+def get_loss(loss_class=None, **kwargs):
+    LossClass = getattr(models, loss_class)
+    loss = LossClass(**kwargs)
+    loss_fn = loss.loss_fn
+    return loss_fn
+
+def get_model(model_class=None, checkpoint_path=None,
+              freeze_loaded_model=False,
+              tensor_args=None,
+              submodules=None,
+              **kwargs):
+    # Handle submodules if any
+    if submodules is not None:
+        for module_name, submodule in submodules.items():
+            kwargs[module_name] = submodule
+
+    # Load or create model
+    if checkpoint_path is not None:
+        model = torch.load(checkpoint_path)
+        if freeze_loaded_model:
+            freeze_torch_model_params(model)
+    else:
+        ModelClass = getattr(models, model_class)
+        model = ModelClass(device=tensor_args['device'], **kwargs).to(tensor_args['device'])
+
+    # Store submodules in model attribute
+    model.submodules = submodules if submodules is not None else {}
+
+    return model
+
+def freeze_torch_model_params(model):
+    for param in model.parameters():
+        param.requires_grad = False
+    return model
+
+
+def get_summary(summary_class=None, **kwargs):
+    if summary_class is None:
+        return None
+    SummaryClass = getattr(summary_trajectory, summary_class)
+    summary_fn = SummaryClass(**kwargs).summary_fn
+    return summary_fn
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/summary_trajectory.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/summary_trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..c130c904c3c6f82a7846c56ea597a40fba1b5a4a
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/summary_trajectory.py
@@ -0,0 +1,77 @@
+import abc
+import numpy as np
+import wandb
+import matplotlib
+matplotlib.use('Agg')  # Set headless backend
+import matplotlib.pyplot as plt
+
+import torch
+
+from cfdp.utils.plot_utils import create_trajectory_figure
+from cfdp.diffusion_policy.models.diffusion_model_base import build_condition
+
+class SummaryBase:
+
+    def __init__(self, **kwargs):
+        pass
+
+    @abc.abstractmethod
+    def summary_fn(self, *args, **kwargs):
+        pass
+
+def generate_trajectories(model, dataset, n_samples, horizon, use_ddim):
+    """
+    Generate and combine ground truth and predicted trajectories.
+    Returns:
+        combined_trajectories: Tensor containing ground truth and predicted trajectories
+                             Shape: (n_samples+1, horizon, dim)
+    """
+    # Get random data and generate predictions
+    data = dataset.get_random_normalized_data(1)[0]
+    context = build_condition(model, dataset, data)
+    hard_conds = data['hard_conds']
+    trajectory = data[f'{dataset.data_key_traj}_normalized']
+    pred_trajs_normalized = model.run_inference(
+        context, hard_conds,
+        n_samples=n_samples, horizon=horizon,
+        deterministic_steps=0,
+        ddim=use_ddim,
+    )        
+    # Move pred_trajs_normalized to CPU and keep as torch tensor
+    pred_trajs_normalized = pred_trajs_normalized.cpu()
+    
+    # Unify trajectory shape handling
+    if len(trajectory.shape) == 2:  # If shape is [L, dim]
+        trajectory = trajectory[None, ...]  # Add batch dimension -> [1, L, dim]
+    # Ensure trajectory is on CPU
+    trajectory = trajectory.cpu()
+    # Combine trajectories using torch.cat instead of np.concatenate
+    combined_trajectories = torch.cat([trajectory, pred_trajs_normalized], dim=0)  # (n_samples+1, 64, 7)
+    return combined_trajectories
+
+class SummaryTrajectoryGeneration(SummaryBase):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.use_ddim = kwargs.get("use_ddim", False)
+        print(f"SummaryTrajectoryGeneration initialized with use_ddim={self.use_ddim}")
+
+    def summary_fn(self, train_step=None, model=None,  datasubset=None, prefix='', debug=False, **kwargs):
+        # summary to wandb
+        dataset = datasubset.dataset
+        n_samples = 20
+        horizon = dataset.trajectory_length
+        
+        combined_trajectories = generate_trajectories(model, dataset, n_samples, horizon, self.use_ddim)
+        fig_ee_pos = create_trajectory_figure(combined_trajectories, 'EE Position'+ " (DDIM)" if self.use_ddim else " (DDPM)")
+
+        # Log figure to wandb
+        if fig_ee_pos is not None:
+            wandb.log({
+                f"{prefix}ee_position_trajectory": wandb.Image(fig_ee_pos),
+            }, step=train_step)
+            # Clean up the figure to prevent memory leaks
+            plt.close(fig_ee_pos)
+        
+            
+        
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/utils.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e6e19451433ff86979224d746d8846b340f4962
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/diffusion_policy/utils/utils.py
@@ -0,0 +1,52 @@
+import torch
+import numpy
+from time import perf_counter
+
+class TimerCUDA:
+    def __init__(self, use_cuda_events=False):
+        self.sync_cuda = torch.cuda.is_available()
+        self.use_cuda_events = use_cuda_events and self.sync_cuda
+        
+        if self.use_cuda_events:
+            self.start_event = None
+            self.end_event = None
+            self.factor_cuda_events = 1./1000.  # ms to seconds
+        else:
+            self.start_time = None
+        
+        self.end = None
+
+    def __call__(self):
+        if self.use_cuda_events:
+            self.end_event.record()
+            torch.cuda.synchronize()
+            return self.start_event.elapsed_time(self.end_event) * self.factor_cuda_events
+        else:
+            if self.sync_cuda:
+                torch.cuda.synchronize()
+            return perf_counter()
+
+    def __enter__(self):
+        if self.use_cuda_events:
+            self.start_event = torch.cuda.Event(enable_timing=True)
+            self.end_event = torch.cuda.Event(enable_timing=True)
+            self.start_event.record()
+        self.start = self()
+        return self
+
+    def __exit__(self, *args):
+        self.end = self()
+
+    @property
+    def elapsed(self):
+        if self.end is None:
+            # During context
+            return self() - self.start
+        else:
+            # After context
+            return self.end - self.start
+
+def to_numpy(tensor):
+    if torch.is_tensor(tensor):
+        return tensor.detach().cpu().numpy()
+    return tensor
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/motion_planner/motion_planner_policy_base.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/motion_planner/motion_planner_policy_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..825b8964724f5e19c3ddd712c33045455f38dea5
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/motion_planner/motion_planner_policy_base.py
@@ -0,0 +1,169 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Dict, Any, Union
+import numpy as np
+import mplib
+from mplib import Pose
+import trimesh
+import matplotlib.pyplot as plt
+import torch
+from mpl_toolkits.mplot3d import Axes3D
+from cfdp.motion_planner.motion_controller import ControllerBase
+class MotionPlannerPolicyBase(ABC):
+    def __init__(self, action_dim, debug=False):
+        """Initialize the base motion planning policy.
+        
+        Args:
+            action_space: The action space of the environment
+            observation_space: The observation space of the environment
+        """
+        # self.action_space = action_space
+        # self.action_dim = action_space.shape[0]
+        self.action_dim = action_dim
+        self.planned_path = None
+        self.current_path_index = 0
+        self.debug = debug
+        # self.fig = None
+        # self.ax = None
+        self.controller = ControllerBase()
+
+    @abstractmethod
+    def plan_path(self, obs: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        pass
+
+    @abstractmethod
+    def reset(self, obstacle_point_cloud: Optional[np.ndarray] = None):
+        """Reset the planner state.
+        
+        Args:
+            obstacle_point_cloud: Optional point cloud of obstacles
+        """
+        pass
+
+    # def compute_control(self, current_state: Dict[str, Any], target_state: np.ndarray, real_robot:bool = False) -> np.ndarray:
+    #     """Generate control action to move from current state to target state.
+    #     """
+    #     current_qpos = current_state['agent']['qpos'][:self.joint_dof]  # Only use the arm joints
+        
+    #     action = target_state - current_qpos
+    #     action = np.pad(action, (0, 2), 'constant', constant_values=0)
+
+    #     if self.current_path_index + 1 < len(self.planned_path):
+    #         self.current_path_index += 1
+    #     return action[:self.action_dim]
+
+    def compute_control(self, current_state: Dict[str, Any], target_state: np.ndarray, real_robot:bool = False) -> np.ndarray:
+        """Generate control action to move from current state to target state.
+        """
+        current_qpos = current_state['agent']['qpos'][:self.joint_dof]  # Only use the arm joints
+
+        action = self.controller.compute_control(current_qpos, target_state)
+
+        if self.current_path_index + 1 < len(self.planned_path):
+            self.current_path_index += 1
+        return action[:self.action_dim]
+
+
+    def __call__(self, obs: Dict[str, Any] ) -> Optional[np.ndarray]:
+        """Generate action based on observation.
+        
+        Args:
+            obs: Current observation from the environment
+            
+        Returns:
+            Optional[np.ndarray]: Action to execute, or None if planning fails
+        """
+        if self.planned_path is None:
+            self.planned_path = self.plan_path(obs)
+            self.current_path_index = 0
+        
+        if self.planned_path is None:
+            return None
+
+        action = self.follow_path(obs)
+
+        # target_state = self.planned_path[self.current_path_index]
+        # action = self.compute_control(obs, target_state)
+        return action
+
+    def follow_path(self, obs: Dict[str, Any], real_robot:bool = False):
+        """Follow the planned path.
+        """
+        if self.planned_path is None:
+            return None
+        
+        target_state = self.planned_path[self.current_path_index]
+        
+        action = self.compute_control(obs, target_state, real_robot = real_robot)
+        return action
+
+    def get_current_path_index(self):
+        return self.current_path_index
+
+
+class ClassicalMotionPlannerPolicy(MotionPlannerPolicyBase):
+    def __init__(self, action_dim, agent, urdf_path, srdf_path, control_timestep, planner_type="screw"):
+        """Initialize the motion planning policy.
+        """
+        super().__init__(action_dim)
+        self.joint_dof = 7
+        self.planner_type = planner_type
+        self.control_timestep = control_timestep
+        self._setup_planner(agent, urdf_path, srdf_path)
+    
+    def _setup_planner(self, agent, urdf_path, srdf_path):
+        #TODO: get link and joint names from agent; or, agent and planner should use same source of configuration
+        link_names = [link.get_name() for link in agent.robot.get_links()]
+        joint_names = [joint.get_name() for joint in agent.robot.get_active_joints()]
+
+        self.planner = mplib.Planner(
+            urdf=urdf_path,
+            # urdf = agent.urdf_path,
+            srdf=srdf_path,
+            user_link_names=link_names,
+            user_joint_names=joint_names,
+            move_group=agent.config.ee_link_name,
+            joint_vel_limits=np.ones(self.joint_dof),
+            joint_acc_limits=np.ones(self.joint_dof))
+        
+    def pose_lst_to_mppose(self, pose):
+        p = np.array(pose[0:3])
+        q = np.array(pose[3:7])
+        return Pose(p, q)
+    
+    def plan_path(self, obs: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+        target_pose = obs['extra']['goal_pose']
+        robot_qpos = obs['agent']['qpos']
+        tcp_pose = obs['extra']['tcp_pose']
+
+        # tcp_pose = obs['extra']['tcp_pose']
+        # print("target_pose: ", target_pose)
+        # print("robot_qpos: ", robot_qpos)
+        # print("tcp_pose: ", tcp_pose)
+
+        # TODO: this rotation should also from observation
+        # target_pose_mpl = Pose(target_pose, [0,1,0,0])
+        target_pose_mpl = Pose(target_pose[0:3], target_pose[3:7])
+
+        # simple screw motion planner
+        if self.planner_type == "screw":
+            result = self.planner.plan_screw(target_pose_mpl, np.array(robot_qpos), time_step=self.control_timestep)
+        # RRT connect
+        elif self.planner_type == "rrt":
+            result = self.planner.plan_pose(target_pose_mpl, np.array(robot_qpos), time_step=self.control_timestep)
+        else:
+            raise ValueError(f"Invalid planner type: {self.planner_type}")
+
+        if result['status'] != "Success":
+            print(f"{self.planner_type} Motion planning failed with status: {result['status']}")
+            return None  # Return None instead of -1 to indicate failure
+        print(f"{self.planner_type} Motion planning succeeded with {len(result['position'])} waypoints")
+        return result['position']
+
+    def reset(self, obstacle_point_cloud = None):
+        self.planned_path = None
+        self.current_path_index = 0
+        if obstacle_point_cloud is not None:
+            self.planner.update_point_cloud(obstacle_point_cloud, resolution=0.02)
+        else:
+            self.planner.remove_point_cloud()
+            # print("No obstacle point cloud provided")
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/readme.md b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/readme.md
new file mode 100644
index 0000000000000000000000000000000000000000..1e2d32f32d490cc5c3697004f4a7ef9d8f9eef42
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/readme.md
@@ -0,0 +1,32 @@
+guidance diffusion algorithm
+
+
+## Dependency:
+```
+pip install mplib
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install open3d
+pip install wandb
+pip install hydra-core --upgrade
+pip install einops
+git clone https://github.com/facebookresearch/pytorch3d.git
+cd pytorch3d & pip install -e .
+pip install scikit-image==0.22.0
+```
+
+
+## Run
+
+### Scene generation:
+Before generation, copy your ycb data set like this:
+```
+rsync -av   --include '*/'   --include 'collision.obj'   --include 'material_0.mtl'   --include 'material_0.png'   --include 'textured.mtl'   --include 'textured.obj'   --exclude '*' {source}/eai-maniskill/data/mani_skill2_ycb/models/ {dest}/cfdp/envs/assets/object/
+```
+
+```
+python tests/test_clutter_env.py --env-id SceneGenerator-v0 --record --distractor-count 10
+# if you want to visualize the generated scene, run:
+python tests/test_clutter_env.py --env-id SceneGenerator-v0 --render --distractor-count 10
+```
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/test_scripts/test_guidance_strength.sh b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/test_scripts/test_guidance_strength.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b1f9671f3fe9583ebdc799502ab889e76aa8cb9b
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/cfdp/test_scripts/test_guidance_strength.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+# Array of guidance weights to test
+# guidance_weights=(0.0 0.1 0.2 0.3 0.4 0.5 0.6)
+
+guidance_weights=(0.0 0.4 0.5 0.6)
+# guidance_weights=(0.8 1.0)
+
+#MPD
+mpd_interval=9999
+replanning_interval=4
+# Loop through each guidance weight
+for weight in "${guidance_weights[@]}"; do
+    echo "----------------------------------------"
+    echo "Testing with guidance weight: $weight"
+    
+    # Run the simulation with the current guidance weight
+    python scripts/sim_benchmark.py \
+        --config-name config_history \
+        inference.guidance_weight=$weight \
+        inference.replanning_interval=$replanning_interval \
+        test.replay_dir="${root_dir}/inference_recordings/guidance_test_${weight}"
+    
+    echo "Completed test with guidance weight: $weight"
+    echo "----------------------------------------"
+done
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/setup.py b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..c462bd0c936b723389e4e13aa83706e04a6a1a39
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/cfdp_experiment/setup.py
@@ -0,0 +1,17 @@
+from setuptools import setup, find_packages
+
+setup(
+    name="cfdp",   # package name on PyPI / pip install
+    version="0.1.0",
+    packages=find_packages(where="."),      # finds cfdp and subpackages
+    include_package_data=True,
+    install_requires=[
+        "mplib",
+        "open3d",
+        "wandb",
+        "hydra-core",
+        "einops",
+        "scikit-image==0.22.0",
+        # PyTorch and PyTorch3D should be installed separately because of CUDA wheels
+    ],
+)
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a8ab061009f1cd87a53ce51eff3bc9e9a632273
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/__init__.py
@@ -0,0 +1,42 @@
+"""
+Test suite for distutils.
+
+Tests for the command classes in the distutils.command package are
+included in distutils.tests as well, instead of using a separate
+distutils.command.tests package, since command identification is done
+by import rather than matching pre-defined names.
+"""
+
+import shutil
+from collections.abc import Sequence
+
+
+def missing_compiler_executable(cmd_names: Sequence[str] = []):  # pragma: no cover
+    """Check if the compiler components used to build the interpreter exist.
+
+    Check for the existence of the compiler executables whose names are listed
+    in 'cmd_names' or all the compiler executables when 'cmd_names' is empty
+    and return the first missing executable or None when none is found
+    missing.
+
+    """
+    from distutils import ccompiler, errors, sysconfig
+
+    compiler = ccompiler.new_compiler()
+    sysconfig.customize_compiler(compiler)
+    if compiler.compiler_type == "msvc":
+        # MSVC has no executables, so check whether initialization succeeds
+        try:
+            compiler.initialize()
+        except errors.DistutilsPlatformError:
+            return "msvc"
+    for name in compiler.executables:
+        if cmd_names and name not in cmd_names:
+            continue
+        cmd = getattr(compiler, name)
+        if cmd_names:
+            assert cmd is not None, f"the '{name}' executable is not configured"
+        elif not cmd:
+            continue
+        if shutil.which(cmd[0]) is None:
+            return cmd[0]
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5696fc3dcd8081335606dc92badd73a69ceac69
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist.py
@@ -0,0 +1,47 @@
+"""Tests for distutils.command.bdist."""
+
+from distutils.command.bdist import bdist
+from distutils.tests import support
+
+
+class TestBuild(support.TempdirManager):
+    def test_formats(self):
+        # let's create a command and make sure
+        # we can set the format
+        dist = self.create_dist()[1]
+        cmd = bdist(dist)
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        assert cmd.formats == ['gztar']
+
+        # what formats does bdist offer?
+        formats = [
+            'bztar',
+            'gztar',
+            'rpm',
+            'tar',
+            'xztar',
+            'zip',
+            'ztar',
+        ]
+        found = sorted(cmd.format_commands)
+        assert found == formats
+
+    def test_skip_build(self):
+        # bug #10946: bdist --skip-build should trickle down to subcommands
+        dist = self.create_dist()[1]
+        cmd = bdist(dist)
+        cmd.skip_build = True
+        cmd.ensure_finalized()
+        dist.command_obj['bdist'] = cmd
+
+        names = [
+            'bdist_dumb',
+        ]  # bdist_rpm does not support --skip-build
+
+        for name in names:
+            subcmd = cmd.get_finalized_command(name)
+            if getattr(subcmd, '_unsupported', False):
+                # command is not supported on this build
+                continue
+            assert subcmd.skip_build, f'{name} should take --skip-build from bdist'
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist_dumb.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist_dumb.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fc51d244a78058329c56a618b092e466715e565
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_bdist_dumb.py
@@ -0,0 +1,78 @@
+"""Tests for distutils.command.bdist_dumb."""
+
+import os
+import sys
+import zipfile
+from distutils.command.bdist_dumb import bdist_dumb
+from distutils.core import Distribution
+from distutils.tests import support
+
+import pytest
+
+SETUP_PY = """\
+from distutils.core import setup
+import foo
+
+setup(name='foo', version='0.1', py_modules=['foo'],
+      url='xxx', author='xxx', author_email='xxx')
+
+"""
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+@pytest.mark.usefixtures('save_argv')
+@pytest.mark.usefixtures('save_cwd')
+class TestBuildDumb(
+    support.TempdirManager,
+):
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_simple_built(self):
+        # let's create a simple package
+        tmp_dir = self.mkdtemp()
+        pkg_dir = os.path.join(tmp_dir, 'foo')
+        os.mkdir(pkg_dir)
+        self.write_file((pkg_dir, 'setup.py'), SETUP_PY)
+        self.write_file((pkg_dir, 'foo.py'), '#')
+        self.write_file((pkg_dir, 'MANIFEST.in'), 'include foo.py')
+        self.write_file((pkg_dir, 'README'), '')
+
+        dist = Distribution({
+            'name': 'foo',
+            'version': '0.1',
+            'py_modules': ['foo'],
+            'url': 'xxx',
+            'author': 'xxx',
+            'author_email': 'xxx',
+        })
+        dist.script_name = 'setup.py'
+        os.chdir(pkg_dir)
+
+        sys.argv = ['setup.py']
+        cmd = bdist_dumb(dist)
+
+        # so the output is the same no matter
+        # what is the platform
+        cmd.format = 'zip'
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # see what we have
+        dist_created = os.listdir(os.path.join(pkg_dir, 'dist'))
+        base = f"{dist.get_fullname()}.{cmd.plat_name}.zip"
+
+        assert dist_created == [base]
+
+        # now let's check what we have in the zip file
+        fp = zipfile.ZipFile(os.path.join('dist', base))
+        try:
+            contents = fp.namelist()
+        finally:
+            fp.close()
+
+        contents = sorted(filter(None, map(os.path.basename, contents)))
+        wanted = ['foo-0.1-py{}.{}.egg-info'.format(*sys.version_info[:2]), 'foo.py']
+        if not sys.dont_write_bytecode:
+            wanted.append(f'foo.{sys.implementation.cache_tag}.pyc')
+        assert contents == sorted(wanted)
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_build_scripts.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_build_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..3582f691ef578a419555c6ffbba6a619310517a6
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_build_scripts.py
@@ -0,0 +1,96 @@
+"""Tests for distutils.command.build_scripts."""
+
+import os
+import textwrap
+from distutils import sysconfig
+from distutils.command.build_scripts import build_scripts
+from distutils.core import Distribution
+from distutils.tests import support
+
+import jaraco.path
+
+
+class TestBuildScripts(support.TempdirManager):
+    def test_default_settings(self):
+        cmd = self.get_build_scripts_cmd("/foo/bar", [])
+        assert not cmd.force
+        assert cmd.build_dir is None
+
+        cmd.finalize_options()
+
+        assert cmd.force
+        assert cmd.build_dir == "/foo/bar"
+
+    def test_build(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+        cmd = self.get_build_scripts_cmd(
+            target, [os.path.join(source, fn) for fn in expected]
+        )
+        cmd.finalize_options()
+        cmd.run()
+
+        built = os.listdir(target)
+        for name in expected:
+            assert name in built
+
+    def get_build_scripts_cmd(self, target, scripts):
+        import sys
+
+        dist = Distribution()
+        dist.scripts = scripts
+        dist.command_obj["build"] = support.DummyCommand(
+            build_scripts=target, force=True, executable=sys.executable
+        )
+        return build_scripts(dist)
+
+    @staticmethod
+    def write_sample_scripts(dir):
+        spec = {
+            'script1.py': textwrap.dedent("""
+                #! /usr/bin/env python2.3
+                # bogus script w/ Python sh-bang
+                pass
+                """).lstrip(),
+            'script2.py': textwrap.dedent("""
+                #!/usr/bin/python
+                # bogus script w/ Python sh-bang
+                pass
+                """).lstrip(),
+            'shell.sh': textwrap.dedent("""
+                #!/bin/sh
+                # bogus shell script w/ sh-bang
+                exit 0
+                """).lstrip(),
+        }
+        jaraco.path.build(spec, dir)
+        return list(spec)
+
+    def test_version_int(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+        cmd = self.get_build_scripts_cmd(
+            target, [os.path.join(source, fn) for fn in expected]
+        )
+        cmd.finalize_options()
+
+        # https://bugs.python.org/issue4524
+        #
+        # On linux-g++-32 with command line `./configure --enable-ipv6
+        # --with-suffix=3`, python is compiled okay but the build scripts
+        # failed when writing the name of the executable
+        old = sysconfig.get_config_vars().get('VERSION')
+        sysconfig._config_vars['VERSION'] = 4
+        try:
+            cmd.run()
+        finally:
+            if old is not None:
+                sysconfig._config_vars['VERSION'] = old
+
+        built = os.listdir(target)
+        for name in expected:
+            assert name in built
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3ffb2e668ac5a042c5a2889d7b51b5755b73157
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install.py
@@ -0,0 +1,245 @@
+"""Tests for distutils.command.install."""
+
+import logging
+import os
+import pathlib
+import site
+import sys
+from distutils import sysconfig
+from distutils.command import install as install_module
+from distutils.command.build_ext import build_ext
+from distutils.command.install import INSTALL_SCHEMES, install
+from distutils.core import Distribution
+from distutils.errors import DistutilsOptionError
+from distutils.extension import Extension
+from distutils.tests import missing_compiler_executable, support
+from distutils.util import is_mingw
+
+import pytest
+
+
+def _make_ext_name(modname):
+    return modname + sysconfig.get_config_var('EXT_SUFFIX')
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+class TestInstall(
+    support.TempdirManager,
+):
+    @pytest.mark.xfail(
+        'platform.system() == "Windows" and sys.version_info > (3, 11)',
+        reason="pypa/distutils#148",
+    )
+    def test_home_installation_scheme(self):
+        # This ensure two things:
+        # - that --home generates the desired set of directory names
+        # - test --home is supported on all platforms
+        builddir = self.mkdtemp()
+        destination = os.path.join(builddir, "installation")
+
+        dist = Distribution({"name": "foopkg"})
+        # script_name need not exist, it just need to be initialized
+        dist.script_name = os.path.join(builddir, "setup.py")
+        dist.command_obj["build"] = support.DummyCommand(
+            build_base=builddir,
+            build_lib=os.path.join(builddir, "lib"),
+        )
+
+        cmd = install(dist)
+        cmd.home = destination
+        cmd.ensure_finalized()
+
+        assert cmd.install_base == destination
+        assert cmd.install_platbase == destination
+
+        def check_path(got, expected):
+            got = os.path.normpath(got)
+            expected = os.path.normpath(expected)
+            assert got == expected
+
+        impl_name = sys.implementation.name.replace("cpython", "python")
+        libdir = os.path.join(destination, "lib", impl_name)
+        check_path(cmd.install_lib, libdir)
+        _platlibdir = getattr(sys, "platlibdir", "lib")
+        platlibdir = os.path.join(destination, _platlibdir, impl_name)
+        check_path(cmd.install_platlib, platlibdir)
+        check_path(cmd.install_purelib, libdir)
+        check_path(
+            cmd.install_headers,
+            os.path.join(destination, "include", impl_name, "foopkg"),
+        )
+        check_path(cmd.install_scripts, os.path.join(destination, "bin"))
+        check_path(cmd.install_data, destination)
+
+    def test_user_site(self, monkeypatch):
+        # test install with --user
+        # preparing the environment for the test
+        self.tmpdir = self.mkdtemp()
+        orig_site = site.USER_SITE
+        orig_base = site.USER_BASE
+        monkeypatch.setattr(site, 'USER_BASE', os.path.join(self.tmpdir, 'B'))
+        monkeypatch.setattr(site, 'USER_SITE', os.path.join(self.tmpdir, 'S'))
+        monkeypatch.setattr(install_module, 'USER_BASE', site.USER_BASE)
+        monkeypatch.setattr(install_module, 'USER_SITE', site.USER_SITE)
+
+        def _expanduser(path):
+            if path.startswith('~'):
+                return os.path.normpath(self.tmpdir + path[1:])
+            return path
+
+        monkeypatch.setattr(os.path, 'expanduser', _expanduser)
+
+        for key in ('nt_user', 'posix_user'):
+            assert key in INSTALL_SCHEMES
+
+        dist = Distribution({'name': 'xx'})
+        cmd = install(dist)
+
+        # making sure the user option is there
+        options = [name for name, short, label in cmd.user_options]
+        assert 'user' in options
+
+        # setting a value
+        cmd.user = True
+
+        # user base and site shouldn't be created yet
+        assert not os.path.exists(site.USER_BASE)
+        assert not os.path.exists(site.USER_SITE)
+
+        # let's run finalize
+        cmd.ensure_finalized()
+
+        # now they should
+        assert os.path.exists(site.USER_BASE)
+        assert os.path.exists(site.USER_SITE)
+
+        assert 'userbase' in cmd.config_vars
+        assert 'usersite' in cmd.config_vars
+
+        actual_headers = os.path.relpath(cmd.install_headers, site.USER_BASE)
+        if os.name == 'nt' and not is_mingw():
+            site_path = os.path.relpath(os.path.dirname(orig_site), orig_base)
+            include = os.path.join(site_path, 'Include')
+        else:
+            include = sysconfig.get_python_inc(0, '')
+        expect_headers = os.path.join(include, 'xx')
+
+        assert os.path.normcase(actual_headers) == os.path.normcase(expect_headers)
+
+    def test_handle_extra_path(self):
+        dist = Distribution({'name': 'xx', 'extra_path': 'path,dirs'})
+        cmd = install(dist)
+
+        # two elements
+        cmd.handle_extra_path()
+        assert cmd.extra_path == ['path', 'dirs']
+        assert cmd.extra_dirs == 'dirs'
+        assert cmd.path_file == 'path'
+
+        # one element
+        cmd.extra_path = ['path']
+        cmd.handle_extra_path()
+        assert cmd.extra_path == ['path']
+        assert cmd.extra_dirs == 'path'
+        assert cmd.path_file == 'path'
+
+        # none
+        dist.extra_path = cmd.extra_path = None
+        cmd.handle_extra_path()
+        assert cmd.extra_path is None
+        assert cmd.extra_dirs == ''
+        assert cmd.path_file is None
+
+        # three elements (no way !)
+        cmd.extra_path = 'path,dirs,again'
+        with pytest.raises(DistutilsOptionError):
+            cmd.handle_extra_path()
+
+    def test_finalize_options(self):
+        dist = Distribution({'name': 'xx'})
+        cmd = install(dist)
+
+        # must supply either prefix/exec-prefix/home or
+        # install-base/install-platbase -- not both
+        cmd.prefix = 'prefix'
+        cmd.install_base = 'base'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        # must supply either home or prefix/exec-prefix -- not both
+        cmd.install_base = None
+        cmd.home = 'home'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        # can't combine user with prefix/exec_prefix/home or
+        # install_(plat)base
+        cmd.prefix = None
+        cmd.user = 'user'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+    def test_record(self):
+        install_dir = self.mkdtemp()
+        project_dir, dist = self.create_dist(py_modules=['hello'], scripts=['sayhi'])
+        os.chdir(project_dir)
+        self.write_file('hello.py', "def main(): print('o hai')")
+        self.write_file('sayhi', 'from hello import main; main()')
+
+        cmd = install(dist)
+        dist.command_obj['install'] = cmd
+        cmd.root = install_dir
+        cmd.record = os.path.join(project_dir, 'filelist')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        content = pathlib.Path(cmd.record).read_text(encoding='utf-8')
+
+        found = [pathlib.Path(line).name for line in content.splitlines()]
+        expected = [
+            'hello.py',
+            f'hello.{sys.implementation.cache_tag}.pyc',
+            'sayhi',
+            'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]),
+        ]
+        assert found == expected
+
+    def test_record_extensions(self):
+        cmd = missing_compiler_executable()
+        if cmd is not None:
+            pytest.skip(f'The {cmd!r} command is not found')
+        install_dir = self.mkdtemp()
+        project_dir, dist = self.create_dist(
+            ext_modules=[Extension('xx', ['xxmodule.c'])]
+        )
+        os.chdir(project_dir)
+        support.copy_xxmodule_c(project_dir)
+
+        buildextcmd = build_ext(dist)
+        support.fixup_build_ext(buildextcmd)
+        buildextcmd.ensure_finalized()
+
+        cmd = install(dist)
+        dist.command_obj['install'] = cmd
+        dist.command_obj['build_ext'] = buildextcmd
+        cmd.root = install_dir
+        cmd.record = os.path.join(project_dir, 'filelist')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        content = pathlib.Path(cmd.record).read_text(encoding='utf-8')
+
+        found = [pathlib.Path(line).name for line in content.splitlines()]
+        expected = [
+            _make_ext_name('xx'),
+            'UNKNOWN-0.0.0-py{}.{}.egg-info'.format(*sys.version_info[:2]),
+        ]
+        assert found == expected
+
+    def test_debug_mode(self, caplog, monkeypatch):
+        # this covers the code called when DEBUG is set
+        monkeypatch.setattr(install_module, 'DEBUG', True)
+        caplog.set_level(logging.DEBUG)
+        self.test_record()
+        assert any(rec for rec in caplog.records if rec.levelno == logging.DEBUG)
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_headers.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_headers.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c74f06b976f3c3ea977667a5c16c0e7f201a558
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_headers.py
@@ -0,0 +1,33 @@
+"""Tests for distutils.command.install_headers."""
+
+import os
+from distutils.command.install_headers import install_headers
+from distutils.tests import support
+
+import pytest
+
+
+@pytest.mark.usefixtures('save_env')
+class TestInstallHeaders(
+    support.TempdirManager,
+):
+    def test_simple_run(self):
+        # we have two headers
+        header_list = self.mkdtemp()
+        header1 = os.path.join(header_list, 'header1')
+        header2 = os.path.join(header_list, 'header2')
+        self.write_file(header1)
+        self.write_file(header2)
+        headers = [header1, header2]
+
+        pkg_dir, dist = self.create_dist(headers=headers)
+        cmd = install_headers(dist)
+        assert cmd.get_inputs() == headers
+
+        # let's run the command
+        cmd.install_dir = os.path.join(pkg_dir, 'inst')
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # let's check the results
+        assert len(cmd.get_outputs()) == 2
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_lib.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_lib.py
new file mode 100644
index 0000000000000000000000000000000000000000..f685a579560ea70bceea221de0e54485975ce180
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_lib.py
@@ -0,0 +1,110 @@
+"""Tests for distutils.command.install_data."""
+
+import importlib.util
+import os
+import sys
+from distutils.command.install_lib import install_lib
+from distutils.errors import DistutilsOptionError
+from distutils.extension import Extension
+from distutils.tests import support
+
+import pytest
+
+
+@support.combine_markers
+@pytest.mark.usefixtures('save_env')
+class TestInstallLib(
+    support.TempdirManager,
+):
+    def test_finalize_options(self):
+        dist = self.create_dist()[1]
+        cmd = install_lib(dist)
+
+        cmd.finalize_options()
+        assert cmd.compile == 1
+        assert cmd.optimize == 0
+
+        # optimize must be 0, 1, or 2
+        cmd.optimize = 'foo'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+        cmd.optimize = '4'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+        cmd.optimize = '2'
+        cmd.finalize_options()
+        assert cmd.optimize == 2
+
+    @pytest.mark.skipif('sys.dont_write_bytecode')
+    def test_byte_compile(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        cmd = install_lib(dist)
+        cmd.compile = cmd.optimize = 1
+
+        f = os.path.join(project_dir, 'foo.py')
+        self.write_file(f, '# python file')
+        cmd.byte_compile([f])
+        pyc_file = importlib.util.cache_from_source('foo.py', optimization='')
+        pyc_opt_file = importlib.util.cache_from_source(
+            'foo.py', optimization=cmd.optimize
+        )
+        assert os.path.exists(pyc_file)
+        assert os.path.exists(pyc_opt_file)
+
+    def test_get_outputs(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        os.mkdir('spam')
+        cmd = install_lib(dist)
+
+        # setting up a dist environment
+        cmd.compile = cmd.optimize = 1
+        cmd.install_dir = self.mkdtemp()
+        f = os.path.join(project_dir, 'spam', '__init__.py')
+        self.write_file(f, '# python package')
+        cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
+        cmd.distribution.packages = ['spam']
+        cmd.distribution.script_name = 'setup.py'
+
+        # get_outputs should return 4 elements: spam/__init__.py and .pyc,
+        # foo.import-tag-abiflags.so / foo.pyd
+        outputs = cmd.get_outputs()
+        assert len(outputs) == 4, outputs
+
+    def test_get_inputs(self):
+        project_dir, dist = self.create_dist()
+        os.chdir(project_dir)
+        os.mkdir('spam')
+        cmd = install_lib(dist)
+
+        # setting up a dist environment
+        cmd.compile = cmd.optimize = 1
+        cmd.install_dir = self.mkdtemp()
+        f = os.path.join(project_dir, 'spam', '__init__.py')
+        self.write_file(f, '# python package')
+        cmd.distribution.ext_modules = [Extension('foo', ['xxx'])]
+        cmd.distribution.packages = ['spam']
+        cmd.distribution.script_name = 'setup.py'
+
+        # get_inputs should return 2 elements: spam/__init__.py and
+        # foo.import-tag-abiflags.so / foo.pyd
+        inputs = cmd.get_inputs()
+        assert len(inputs) == 2, inputs
+
+    def test_dont_write_bytecode(self, caplog):
+        # makes sure byte_compile is not used
+        dist = self.create_dist()[1]
+        cmd = install_lib(dist)
+        cmd.compile = True
+        cmd.optimize = 1
+
+        old_dont_write_bytecode = sys.dont_write_bytecode
+        sys.dont_write_bytecode = True
+        try:
+            cmd.byte_compile([])
+        finally:
+            sys.dont_write_bytecode = old_dont_write_bytecode
+
+        assert 'byte-compiling is disabled' in caplog.messages[0]
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_scripts.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_scripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..868b1c22521f23a5dd398e4636c94330d4c238ac
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_install_scripts.py
@@ -0,0 +1,52 @@
+"""Tests for distutils.command.install_scripts."""
+
+import os
+from distutils.command.install_scripts import install_scripts
+from distutils.core import Distribution
+from distutils.tests import support
+
+from . import test_build_scripts
+
+
+class TestInstallScripts(support.TempdirManager):
+    def test_default_settings(self):
+        dist = Distribution()
+        dist.command_obj["build"] = support.DummyCommand(build_scripts="/foo/bar")
+        dist.command_obj["install"] = support.DummyCommand(
+            install_scripts="/splat/funk",
+            force=True,
+            skip_build=True,
+        )
+        cmd = install_scripts(dist)
+        assert not cmd.force
+        assert not cmd.skip_build
+        assert cmd.build_dir is None
+        assert cmd.install_dir is None
+
+        cmd.finalize_options()
+
+        assert cmd.force
+        assert cmd.skip_build
+        assert cmd.build_dir == "/foo/bar"
+        assert cmd.install_dir == "/splat/funk"
+
+    def test_installation(self):
+        source = self.mkdtemp()
+
+        expected = test_build_scripts.TestBuildScripts.write_sample_scripts(source)
+
+        target = self.mkdtemp()
+        dist = Distribution()
+        dist.command_obj["build"] = support.DummyCommand(build_scripts=source)
+        dist.command_obj["install"] = support.DummyCommand(
+            install_scripts=target,
+            force=True,
+            skip_build=True,
+        )
+        cmd = install_scripts(dist)
+        cmd.finalize_options()
+        cmd.run()
+
+        installed = os.listdir(target)
+        for name in expected:
+            assert name in installed
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_sdist.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_sdist.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b1a376b262162aa7c8d3d989709c05b38563d6c
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_sdist.py
@@ -0,0 +1,470 @@
+"""Tests for distutils.command.sdist."""
+
+import os
+import pathlib
+import shutil  # noqa: F401
+import tarfile
+import zipfile
+from distutils.archive_util import ARCHIVE_FORMATS
+from distutils.command.sdist import sdist, show_formats
+from distutils.core import Distribution
+from distutils.errors import DistutilsOptionError
+from distutils.filelist import FileList
+from os.path import join
+from textwrap import dedent
+
+import jaraco.path
+import path
+import pytest
+from more_itertools import ilen
+
+from . import support
+from .unix_compat import grp, pwd, require_uid_0, require_unix_id
+
+SETUP_PY = """
+from distutils.core import setup
+import somecode
+
+setup(name='fake')
+"""
+
+MANIFEST = """\
+# file GENERATED by distutils, do NOT edit
+README
+buildout.cfg
+inroot.txt
+setup.py
+data%(sep)sdata.dt
+scripts%(sep)sscript.py
+some%(sep)sfile.txt
+some%(sep)sother_file.txt
+somecode%(sep)s__init__.py
+somecode%(sep)sdoc.dat
+somecode%(sep)sdoc.txt
+"""
+
+
+@pytest.fixture(autouse=True)
+def project_dir(request, distutils_managed_tempdir):
+    self = request.instance
+    self.tmp_dir = self.mkdtemp()
+    jaraco.path.build(
+        {
+            'somecode': {
+                '__init__.py': '#',
+            },
+            'README': 'xxx',
+            'setup.py': SETUP_PY,
+        },
+        self.tmp_dir,
+    )
+    with path.Path(self.tmp_dir):
+        yield
+
+
+def clean_lines(filepath):
+    with pathlib.Path(filepath).open(encoding='utf-8') as f:
+        yield from filter(None, map(str.strip, f))
+
+
+class TestSDist(support.TempdirManager):
+    def get_cmd(self, metadata=None):
+        """Returns a cmd"""
+        if metadata is None:
+            metadata = {
+                'name': 'ns.fake--pkg',
+                'version': '1.0',
+                'url': 'xxx',
+                'author': 'xxx',
+                'author_email': 'xxx',
+            }
+        dist = Distribution(metadata)
+        dist.script_name = 'setup.py'
+        dist.packages = ['somecode']
+        dist.include_package_data = True
+        cmd = sdist(dist)
+        cmd.dist_dir = 'dist'
+        return dist, cmd
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_prune_file_list(self):
+        # this test creates a project with some VCS dirs and an NFS rename
+        # file, then launches sdist to check they get pruned on all systems
+
+        # creating VCS directories with some files in them
+        os.mkdir(join(self.tmp_dir, 'somecode', '.svn'))
+        self.write_file((self.tmp_dir, 'somecode', '.svn', 'ok.py'), 'xxx')
+
+        os.mkdir(join(self.tmp_dir, 'somecode', '.hg'))
+        self.write_file((self.tmp_dir, 'somecode', '.hg', 'ok'), 'xxx')
+
+        os.mkdir(join(self.tmp_dir, 'somecode', '.git'))
+        self.write_file((self.tmp_dir, 'somecode', '.git', 'ok'), 'xxx')
+
+        self.write_file((self.tmp_dir, 'somecode', '.nfs0001'), 'xxx')
+
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # zip is available universally
+        # (tar might not be installed under win32)
+        cmd.formats = ['zip']
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # now let's check what we have
+        dist_folder = join(self.tmp_dir, 'dist')
+        files = os.listdir(dist_folder)
+        assert files == ['ns_fake_pkg-1.0.zip']
+
+        zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip'))
+        try:
+            content = zip_file.namelist()
+        finally:
+            zip_file.close()
+
+        # making sure everything has been pruned correctly
+        expected = [
+            '',
+            'PKG-INFO',
+            'README',
+            'setup.py',
+            'somecode/',
+            'somecode/__init__.py',
+        ]
+        assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @pytest.mark.skipif("not shutil.which('tar')")
+    @pytest.mark.skipif("not shutil.which('gzip')")
+    def test_make_distribution(self):
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar then a tar
+        cmd.formats = ['gztar', 'tar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have two files
+        dist_folder = join(self.tmp_dir, 'dist')
+        result = os.listdir(dist_folder)
+        result.sort()
+        assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz']
+
+        os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar'))
+        os.remove(join(dist_folder, 'ns_fake_pkg-1.0.tar.gz'))
+
+        # now trying a tar then a gztar
+        cmd.formats = ['tar', 'gztar']
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        result = os.listdir(dist_folder)
+        result.sort()
+        assert result == ['ns_fake_pkg-1.0.tar', 'ns_fake_pkg-1.0.tar.gz']
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_add_defaults(self):
+        # https://bugs.python.org/issue2279
+
+        # add_default should also include
+        # data_files and package_data
+        dist, cmd = self.get_cmd()
+
+        # filling data_files by pointing files
+        # in package_data
+        dist.package_data = {'': ['*.cfg', '*.dat'], 'somecode': ['*.txt']}
+        self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#')
+        self.write_file((self.tmp_dir, 'somecode', 'doc.dat'), '#')
+
+        # adding some data in data_files
+        data_dir = join(self.tmp_dir, 'data')
+        os.mkdir(data_dir)
+        self.write_file((data_dir, 'data.dt'), '#')
+        some_dir = join(self.tmp_dir, 'some')
+        os.mkdir(some_dir)
+        # make sure VCS directories are pruned (#14004)
+        hg_dir = join(self.tmp_dir, '.hg')
+        os.mkdir(hg_dir)
+        self.write_file((hg_dir, 'last-message.txt'), '#')
+        # a buggy regex used to prevent this from working on windows (#6884)
+        self.write_file((self.tmp_dir, 'buildout.cfg'), '#')
+        self.write_file((self.tmp_dir, 'inroot.txt'), '#')
+        self.write_file((some_dir, 'file.txt'), '#')
+        self.write_file((some_dir, 'other_file.txt'), '#')
+
+        dist.data_files = [
+            ('data', ['data/data.dt', 'buildout.cfg', 'inroot.txt', 'notexisting']),
+            'some/file.txt',
+            'some/other_file.txt',
+        ]
+
+        # adding a script
+        script_dir = join(self.tmp_dir, 'scripts')
+        os.mkdir(script_dir)
+        self.write_file((script_dir, 'script.py'), '#')
+        dist.scripts = [join('scripts', 'script.py')]
+
+        cmd.formats = ['zip']
+        cmd.use_defaults = True
+
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # now let's check what we have
+        dist_folder = join(self.tmp_dir, 'dist')
+        files = os.listdir(dist_folder)
+        assert files == ['ns_fake_pkg-1.0.zip']
+
+        zip_file = zipfile.ZipFile(join(dist_folder, 'ns_fake_pkg-1.0.zip'))
+        try:
+            content = zip_file.namelist()
+        finally:
+            zip_file.close()
+
+        # making sure everything was added
+        expected = [
+            '',
+            'PKG-INFO',
+            'README',
+            'buildout.cfg',
+            'data/',
+            'data/data.dt',
+            'inroot.txt',
+            'scripts/',
+            'scripts/script.py',
+            'setup.py',
+            'some/',
+            'some/file.txt',
+            'some/other_file.txt',
+            'somecode/',
+            'somecode/__init__.py',
+            'somecode/doc.dat',
+            'somecode/doc.txt',
+        ]
+        assert sorted(content) == ['ns_fake_pkg-1.0/' + x for x in expected]
+
+        # checking the MANIFEST
+        manifest = pathlib.Path(self.tmp_dir, 'MANIFEST').read_text(encoding='utf-8')
+        assert manifest == MANIFEST % {'sep': os.sep}
+
+    @staticmethod
+    def warnings(messages, prefix='warning: '):
+        return [msg for msg in messages if msg.startswith(prefix)]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_metadata_check_option(self, caplog):
+        # testing the `medata-check` option
+        dist, cmd = self.get_cmd(metadata={})
+
+        # this should raise some warnings !
+        # with the `check` subcommand
+        cmd.ensure_finalized()
+        cmd.run()
+        assert len(self.warnings(caplog.messages, 'warning: check: ')) == 1
+
+        # trying with a complete set of metadata
+        caplog.clear()
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        cmd.metadata_check = False
+        cmd.run()
+        assert len(self.warnings(caplog.messages, 'warning: check: ')) == 0
+
+    def test_show_formats(self, capsys):
+        show_formats()
+
+        # the output should be a header line + one line per format
+        num_formats = len(ARCHIVE_FORMATS.keys())
+        output = [
+            line
+            for line in capsys.readouterr().out.split('\n')
+            if line.strip().startswith('--formats=')
+        ]
+        assert len(output) == num_formats
+
+    def test_finalize_options(self):
+        dist, cmd = self.get_cmd()
+        cmd.finalize_options()
+
+        # default options set by finalize
+        assert cmd.manifest == 'MANIFEST'
+        assert cmd.template == 'MANIFEST.in'
+        assert cmd.dist_dir == 'dist'
+
+        # formats has to be a string splitable on (' ', ',') or
+        # a stringlist
+        cmd.formats = 1
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+        cmd.formats = ['zip']
+        cmd.finalize_options()
+
+        # formats has to be known
+        cmd.formats = 'supazipa'
+        with pytest.raises(DistutilsOptionError):
+            cmd.finalize_options()
+
+    # the following tests make sure there is a nice error message instead
+    # of a traceback when parsing an invalid manifest template
+
+    def _check_template(self, content, caplog):
+        dist, cmd = self.get_cmd()
+        os.chdir(self.tmp_dir)
+        self.write_file('MANIFEST.in', content)
+        cmd.ensure_finalized()
+        cmd.filelist = FileList()
+        cmd.read_template()
+        assert len(self.warnings(caplog.messages)) == 1
+
+    def test_invalid_template_unknown_command(self, caplog):
+        self._check_template('taunt knights *', caplog)
+
+    def test_invalid_template_wrong_arguments(self, caplog):
+        # this manifest command takes one argument
+        self._check_template('prune', caplog)
+
+    @pytest.mark.skipif("platform.system() != 'Windows'")
+    def test_invalid_template_wrong_path(self, caplog):
+        # on Windows, trailing slashes are not allowed
+        # this used to crash instead of raising a warning: #8286
+        self._check_template('include examples/', caplog)
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_get_file_list(self):
+        # make sure MANIFEST is recalculated
+        dist, cmd = self.get_cmd()
+
+        # filling data_files by pointing files in package_data
+        dist.package_data = {'somecode': ['*.txt']}
+        self.write_file((self.tmp_dir, 'somecode', 'doc.txt'), '#')
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        assert ilen(clean_lines(cmd.manifest)) == 5
+
+        # adding a file
+        self.write_file((self.tmp_dir, 'somecode', 'doc2.txt'), '#')
+
+        # make sure build_py is reinitialized, like a fresh run
+        build_py = dist.get_command_obj('build_py')
+        build_py.finalized = False
+        build_py.ensure_finalized()
+
+        cmd.run()
+
+        manifest2 = list(clean_lines(cmd.manifest))
+
+        # do we have the new file in MANIFEST ?
+        assert len(manifest2) == 6
+        assert 'doc2.txt' in manifest2[-1]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manifest_marker(self):
+        # check that autogenerated MANIFESTs have a marker
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        cmd.run()
+
+        assert (
+            next(clean_lines(cmd.manifest))
+            == '# file GENERATED by distutils, do NOT edit'
+        )
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manifest_comments(self):
+        # make sure comments don't cause exceptions or wrong includes
+        contents = dedent(
+            """\
+            # bad.py
+            #bad.py
+            good.py
+            """
+        )
+        dist, cmd = self.get_cmd()
+        cmd.ensure_finalized()
+        self.write_file((self.tmp_dir, cmd.manifest), contents)
+        self.write_file((self.tmp_dir, 'good.py'), '# pick me!')
+        self.write_file((self.tmp_dir, 'bad.py'), "# don't pick me!")
+        self.write_file((self.tmp_dir, '#bad.py'), "# don't pick me!")
+        cmd.run()
+        assert cmd.filelist.files == ['good.py']
+
+    @pytest.mark.usefixtures('needs_zlib')
+    def test_manual_manifest(self):
+        # check that a MANIFEST without a marker is left alone
+        dist, cmd = self.get_cmd()
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        self.write_file((self.tmp_dir, cmd.manifest), 'README.manual')
+        self.write_file(
+            (self.tmp_dir, 'README.manual'),
+            'This project maintains its MANIFEST file itself.',
+        )
+        cmd.run()
+        assert cmd.filelist.files == ['README.manual']
+
+        assert list(clean_lines(cmd.manifest)) == ['README.manual']
+
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+        try:
+            filenames = [tarinfo.name for tarinfo in archive]
+        finally:
+            archive.close()
+        assert sorted(filenames) == [
+            'ns_fake_pkg-1.0',
+            'ns_fake_pkg-1.0/PKG-INFO',
+            'ns_fake_pkg-1.0/README.manual',
+        ]
+
+    @pytest.mark.usefixtures('needs_zlib')
+    @require_unix_id
+    @require_uid_0
+    @pytest.mark.skipif("not shutil.which('tar')")
+    @pytest.mark.skipif("not shutil.which('gzip')")
+    def test_make_distribution_owner_group(self):
+        # now building a sdist
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar and specifying the owner+group
+        cmd.formats = ['gztar']
+        cmd.owner = pwd.getpwuid(0)[0]
+        cmd.group = grp.getgrgid(0)[0]
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have the good rights
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+        try:
+            for member in archive.getmembers():
+                assert member.uid == 0
+                assert member.gid == 0
+        finally:
+            archive.close()
+
+        # building a sdist again
+        dist, cmd = self.get_cmd()
+
+        # creating a gztar
+        cmd.formats = ['gztar']
+        cmd.ensure_finalized()
+        cmd.run()
+
+        # making sure we have the good rights
+        archive_name = join(self.tmp_dir, 'dist', 'ns_fake_pkg-1.0.tar.gz')
+        archive = tarfile.open(archive_name)
+
+        # note that we are not testing the group ownership here
+        # because, depending on the platforms and the container
+        # rights (see #7408)
+        try:
+            for member in archive.getmembers():
+                assert member.uid == os.getuid()
+        finally:
+            archive.close()
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_text_file.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_text_file.py
new file mode 100644
index 0000000000000000000000000000000000000000..f51115656135cd3f348a45d4a14f97eeee47fb98
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_text_file.py
@@ -0,0 +1,127 @@
+"""Tests for distutils.text_file."""
+
+from distutils.tests import support
+from distutils.text_file import TextFile
+
+import jaraco.path
+import path
+
+TEST_DATA = """# test file
+
+line 3 \\
+# intervening comment
+  continues on next line
+"""
+
+
+class TestTextFile(support.TempdirManager):
+    def test_class(self):
+        # old tests moved from text_file.__main__
+        # so they are really called by the buildbots
+
+        # result 1: no fancy options
+        result1 = [
+            '# test file\n',
+            '\n',
+            'line 3 \\\n',
+            '# intervening comment\n',
+            '  continues on next line\n',
+        ]
+
+        # result 2: just strip comments
+        result2 = ["\n", "line 3 \\\n", "  continues on next line\n"]
+
+        # result 3: just strip blank lines
+        result3 = [
+            "# test file\n",
+            "line 3 \\\n",
+            "# intervening comment\n",
+            "  continues on next line\n",
+        ]
+
+        # result 4: default, strip comments, blank lines,
+        # and trailing whitespace
+        result4 = ["line 3 \\", "  continues on next line"]
+
+        # result 5: strip comments and blanks, plus join lines (but don't
+        # "collapse" joined lines
+        result5 = ["line 3   continues on next line"]
+
+        # result 6: strip comments and blanks, plus join lines (and
+        # "collapse" joined lines
+        result6 = ["line 3 continues on next line"]
+
+        def test_input(count, description, file, expected_result):
+            result = file.readlines()
+            assert result == expected_result
+
+        tmp_path = path.Path(self.mkdtemp())
+        filename = tmp_path / 'test.txt'
+        jaraco.path.build({filename.name: TEST_DATA}, tmp_path)
+
+        in_file = TextFile(
+            filename,
+            strip_comments=False,
+            skip_blanks=False,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(1, "no processing", in_file, result1)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=False,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(2, "strip comments", in_file, result2)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=False,
+            skip_blanks=True,
+            lstrip_ws=False,
+            rstrip_ws=False,
+        )
+        try:
+            test_input(3, "strip blanks", in_file, result3)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(filename)
+        try:
+            test_input(4, "default processing", in_file, result4)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=True,
+            join_lines=True,
+            rstrip_ws=True,
+        )
+        try:
+            test_input(5, "join lines without collapsing", in_file, result5)
+        finally:
+            in_file.close()
+
+        in_file = TextFile(
+            filename,
+            strip_comments=True,
+            skip_blanks=True,
+            join_lines=True,
+            rstrip_ws=True,
+            collapse_join=True,
+        )
+        try:
+            test_input(6, "join lines with collapsing", in_file, result6)
+        finally:
+            in_file.close()
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_version.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..b68f0977249aa23aafbf31cf74a0e4127f777d05
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_version.py
@@ -0,0 +1,80 @@
+"""Tests for distutils.version."""
+
+import distutils
+from distutils.version import LooseVersion, StrictVersion
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def suppress_deprecation():
+    with distutils.version.suppress_known_deprecation():
+        yield
+
+
+class TestVersion:
+    def test_prerelease(self):
+        version = StrictVersion('1.2.3a1')
+        assert version.version == (1, 2, 3)
+        assert version.prerelease == ('a', 1)
+        assert str(version) == '1.2.3a1'
+
+        version = StrictVersion('1.2.0')
+        assert str(version) == '1.2'
+
+    def test_cmp_strict(self):
+        versions = (
+            ('1.5.1', '1.5.2b2', -1),
+            ('161', '3.10a', ValueError),
+            ('8.02', '8.02', 0),
+            ('3.4j', '1996.07.12', ValueError),
+            ('3.2.pl0', '3.1.1.6', ValueError),
+            ('2g6', '11g', ValueError),
+            ('0.9', '2.2', -1),
+            ('1.2.1', '1.2', 1),
+            ('1.1', '1.2.2', -1),
+            ('1.2', '1.1', 1),
+            ('1.2.1', '1.2.2', -1),
+            ('1.2.2', '1.2', 1),
+            ('1.2', '1.2.2', -1),
+            ('0.4.0', '0.4', 0),
+            ('1.13++', '5.5.kw', ValueError),
+        )
+
+        for v1, v2, wanted in versions:
+            try:
+                res = StrictVersion(v1)._cmp(StrictVersion(v2))
+            except ValueError:
+                if wanted is ValueError:
+                    continue
+                else:
+                    raise AssertionError(f"cmp({v1}, {v2}) shouldn't raise ValueError")
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = StrictVersion(v1)._cmp(v2)
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = StrictVersion(v1)._cmp(object())
+            assert res is NotImplemented, (
+                f'cmp({v1}, {v2}) should be NotImplemented, got {res}'
+            )
+
+    def test_cmp(self):
+        versions = (
+            ('1.5.1', '1.5.2b2', -1),
+            ('161', '3.10a', 1),
+            ('8.02', '8.02', 0),
+            ('3.4j', '1996.07.12', -1),
+            ('3.2.pl0', '3.1.1.6', 1),
+            ('2g6', '11g', -1),
+            ('0.960923', '2.2beta29', -1),
+            ('1.13++', '5.5.kw', -1),
+        )
+
+        for v1, v2, wanted in versions:
+            res = LooseVersion(v1)._cmp(LooseVersion(v2))
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = LooseVersion(v1)._cmp(v2)
+            assert res == wanted, f'cmp({v1}, {v2}) should be {wanted}, got {res}'
+            res = LooseVersion(v1)._cmp(object())
+            assert res is NotImplemented, (
+                f'cmp({v1}, {v2}) should be NotImplemented, got {res}'
+            )
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_versionpredicate.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/test_versionpredicate.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/unix_compat.py b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/unix_compat.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d9ee45ccc639794d29ba0a5fd24f781ee5ecf5
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/mani_skill2_repo/.eggs/setuptools-80.9.0-py3.10.egg/setuptools/_distutils/tests/unix_compat.py
@@ -0,0 +1,17 @@
+import sys
+
+try:
+    import grp
+    import pwd
+except ImportError:
+    grp = pwd = None
+
+import pytest
+
+UNIX_ID_SUPPORT = grp and pwd
+UID_0_SUPPORT = UNIX_ID_SUPPORT and sys.platform != "cygwin"
+
+require_unix_id = pytest.mark.skipif(
+    not UNIX_ID_SUPPORT, reason="Requires grp and pwd support"
+)
+require_uid_0 = pytest.mark.skipif(not UID_0_SUPPORT, reason="Requires UID 0 support")
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/15_scene_config.yaml b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/15_scene_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f3d3d1d23540e989326f962918fad67fc779277
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/15_scene_config.yaml
@@ -0,0 +1,458 @@
+objects:
+- collision:
+    path: object/table/desktable.obj
+  name: table
+  pose:
+    position:
+    - 0.4000000059604645
+    - 0.0
+    - -0.75
+    quaternion:
+    - 0.0
+    - 0.0
+    - 0.7071068286895752
+    - 0.7071068286895752
+  scale:
+  - 2.0
+  - 1.0
+  - 1.5
+  visual:
+    path: object/table/desktable.glb
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_01
+  pose:
+    position:
+    - 0.4863060414791107
+    - -0.02154911682009697
+    - 0.19764424860477448
+    quaternion:
+    - 0.9768074750900269
+    - -4.656612873077393e-10
+    - -1.65601932167192e-09
+    - 0.21412016451358795
+  scale:
+  - 0.43156745678016356
+  - 0.4938321481829596
+  - 1.0544241940054395
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_02
+  pose:
+    position:
+    - 0.5038949251174927
+    - -0.3304351568222046
+    - 0.12995554506778717
+    quaternion:
+    - 0.9470155835151672
+    - -9.313225746154785e-10
+    - 7.623384590260684e-09
+    - 0.32118794322013855
+  scale:
+  - 0.40738405359814406
+  - 0.5761527279656022
+  - 0.7179754719214234
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/051_large_clamp/collision.obj
+  name: large_clamp
+  pose:
+    position:
+    - 0.4263603985309601
+    - 0.2136603742837906
+    - 0.00481343874707818
+    quaternion:
+    - 0.7614054679870605
+    - -0.047055039554834366
+    - 0.021931948140263557
+    - -0.6461939811706543
+  scale: 1.0
+  visual:
+    path: object/051_large_clamp/textured.obj
+- collision:
+    path: object/032_knife/collision.obj
+  name: knife
+  pose:
+    position:
+    - 0.19608545303344727
+    - 0.07220133394002914
+    - -0.00670318678021431
+    quaternion:
+    - 0.7531771063804626
+    - -0.044308505952358246
+    - -0.03745051100850105
+    - 0.655254602432251
+  scale: 1.0
+  visual:
+    path: object/032_knife/textured.obj
+- collision:
+    path: object/038_padlock/collision.obj
+  name: padlock
+  pose:
+    position:
+    - 0.12793344259262085
+    - 0.0077934106811881065
+    - -0.0025017347652465105
+    quaternion:
+    - 0.09830980002880096
+    - 0.6553937792778015
+    - -0.7485232353210449
+    - 0.02252737432718277
+  scale: 1.0
+  visual:
+    path: object/038_padlock/textured.obj
+- collision:
+    path: object/005_tomato_soup_can/collision.obj
+  name: tomato_soup_can
+  pose:
+    position:
+    - 0.6074629426002502
+    - -0.19723476469516754
+    - 0.036737360060214996
+    quaternion:
+    - 0.8581460118293762
+    - 0.004277520813047886
+    - 0.0020168512128293514
+    - 0.5133838653564453
+  scale: 1.0
+  visual:
+    path: object/005_tomato_soup_can/textured.obj
+- collision:
+    path: object/057_racquetball/collision.obj
+  name: racquetball
+  pose:
+    position:
+    - 0.07873370498418808
+    - -0.2952410578727722
+    - 0.013450687751173973
+    quaternion:
+    - 0.7480661869049072
+    - -0.05817950516939163
+    - 0.3907582759857178
+    - -0.5332170128822327
+  scale: 1.0
+  visual:
+    path: object/057_racquetball/textured.obj
+- collision:
+    path: object/054_softball/collision.obj
+  name: softball
+  pose:
+    position:
+    - 0.2731949985027313
+    - -0.24295438826084137
+    - 0.03371398150920868
+    quaternion:
+    - -0.019237220287322998
+    - -0.9334341883659363
+    - 0.3443043529987335
+    - -0.09892145544290543
+  scale: 1.0
+  visual:
+    path: object/054_softball/textured.obj
+- collision:
+    path: object/009_gelatin_box/collision.obj
+  name: gelatin_box
+  pose:
+    position:
+    - 0.539089024066925
+    - -0.3831791281700134
+    - 0.0009584762738086283
+    quaternion:
+    - 0.8721807599067688
+    - 5.44136855751276e-06
+    - -3.0881710699759424e-06
+    - 0.489183634519577
+  scale: 1.0
+  visual:
+    path: object/009_gelatin_box/textured.obj
+- collision:
+    path: object/011_banana/collision.obj
+  name: banana
+  pose:
+    position:
+    - 0.4982348680496216
+    - 0.37507933378219604
+    - 0.010817192494869232
+    quaternion:
+    - 0.9843353033065796
+    - -0.05041609704494476
+    - -0.10428669303655624
+    - -0.13291530311107635
+  scale: 1.0
+  visual:
+    path: object/011_banana/textured.obj
+- collision:
+    path: object/043_phillips_screwdriver/collision.obj
+  name: phillips_screwdriver
+  pose:
+    position:
+    - 0.15374013781547546
+    - 0.41787078976631165
+    - 0.0012195908930152655
+    quaternion:
+    - -0.3016364872455597
+    - -0.9529399871826172
+    - 0.029105165973305702
+    - -0.008580973371863365
+  scale: 1.0
+  visual:
+    path: object/043_phillips_screwdriver/textured.obj
+- collision:
+    path: object/073-a_lego_duplo/collision.obj
+  name: lego_duplo
+  pose:
+    position:
+    - 0.05934309959411621
+    - -0.47076714038848877
+    - -0.0019606705754995346
+    quaternion:
+    - 0.99864262342453
+    - -7.86907912697643e-06
+    - -8.23449227027595e-06
+    - 0.052087221294641495
+  scale: 1.0
+  visual:
+    path: object/073-a_lego_duplo/textured.obj
+- collision:
+    path: object/006_mustard_bottle/collision.obj
+  name: mustard_bottle
+  pose:
+    position:
+    - 0.09847545623779297
+    - 0.2050512731075287
+    - 0.08319114148616791
+    quaternion:
+    - 0.9925246238708496
+    - -0.003396711079403758
+    - 0.0003828755288850516
+    - -0.12199683487415314
+  scale: 1.0
+  visual:
+    path: object/006_mustard_bottle/textured.obj
+- collision:
+    path: object/002_master_chef_can/collision.obj
+  name: master_chef_can
+  pose:
+    position:
+    - 0.5520784258842468
+    - 0.07806409150362015
+    - 0.4756503999233246
+    quaternion:
+    - 0.9842634201049805
+    - 0.0010863442439585924
+    - 0.0083326306194067
+    - 0.1765078753232956
+  scale: 1.0
+  visual:
+    path: object/002_master_chef_can/textured.obj
+- collision:
+    path: object/036_wood_block/collision.obj
+  name: wood_block
+  pose:
+    position:
+    - 0.2083340287208557
+    - 0.506116509437561
+    - 0.03112698718905449
+    quaternion:
+    - 0.384711354970932
+    - 0.39756035804748535
+    - -0.5848114490509033
+    - -0.5932443141937256
+  scale: 1.0
+  visual:
+    path: object/036_wood_block/textured.obj
+- collision:
+    path: object/012_strawberry/collision.obj
+  name: strawberry
+  pose:
+    position:
+    - 0.24706460535526276
+    - -0.48446232080459595
+    - 0.008575457148253918
+    quaternion:
+    - 0.9794702529907227
+    - -0.013333138078451157
+    - 0.028693951666355133
+    - -0.19909067451953888
+  scale: 1.0
+  visual:
+    path: object/012_strawberry/textured.obj
+- collision:
+    path: object/072-c_toy_airplane/collision.obj
+  name: toy_airplane
+  pose:
+    position:
+    - 0.22543838620185852
+    - -0.129425510764122
+    - 0.0005092824576422572
+    quaternion:
+    - 0.653056263923645
+    - 0.35420021414756775
+    - -0.6225707530975342
+    - 0.2458970993757248
+  scale: 1.0
+  visual:
+    path: object/072-c_toy_airplane/textured.obj
+- collision:
+    path: object/015_peach/collision.obj
+  name: peach
+  pose:
+    position:
+    - 0.6195400953292847
+    - 0.2319686859846115
+    - 0.01432133186608553
+    quaternion:
+    - 0.7804426550865173
+    - 0.14326684176921844
+    - -0.19445189833641052
+    - 0.5766910314559937
+  scale: 1.0
+  visual:
+    path: object/015_peach/textured.obj
+- collision:
+    path: object/014_lemon/collision.obj
+  name: lemon
+  pose:
+    position:
+    - 0.6684139966964722
+    - 0.3725297749042511
+    - 0.012238416820764542
+    quaternion:
+    - 0.9557560682296753
+    - 7.022544741630554e-05
+    - 1.2643635272979736e-05
+    - 0.2941604554653168
+  scale: 1.0
+  visual:
+    path: object/014_lemon/textured.obj
+- collision:
+    path: object/055_baseball/collision.obj
+  name: baseball
+  pose:
+    position:
+    - 0.6715689897537231
+    - 0.4593181610107422
+    - 0.021909475326538086
+    quaternion:
+    - 0.9192414879798889
+    - -0.03221141919493675
+    - 0.010357033461332321
+    - 0.39223751425743103
+  scale: 1.0
+  visual:
+    path: object/055_baseball/textured.obj
+- collision:
+    path: object/026_sponge/collision.obj
+  name: sponge
+  pose:
+    position:
+    - 0.5018317699432373
+    - -0.12260034680366516
+    - 0.04094848036766052
+    quaternion:
+    - 0.7452517747879028
+    - -0.6294000744819641
+    - -0.14290474355220795
+    - 0.16743262112140656
+  scale: 1.0
+  visual:
+    path: object/026_sponge/textured.obj
+- collision:
+    path: object/007_tuna_fish_can/collision.obj
+  name: tuna_fish_can
+  pose:
+    position:
+    - 0.6762484908103943
+    - -0.34881848096847534
+    - 0.002806073985993862
+    quaternion:
+    - 0.8151469826698303
+    - -0.0006976943113841116
+    - 0.0003048336657229811
+    - 0.5792537331581116
+  scale: 1.0
+  visual:
+    path: object/007_tuna_fish_can/textured.obj
+- collision:
+    path: object/017_orange/collision.obj
+  name: orange
+  pose:
+    position:
+    - 0.4282000660896301
+    - -0.15128178894519806
+    - 0.023155396804213524
+    quaternion:
+    - -0.13459272682666779
+    - 0.40583541989326477
+    - 0.4616560935974121
+    - -0.777210533618927
+  scale: 1.0
+  visual:
+    path: object/017_orange/textured.obj
+- collision:
+    path: object/033_spatula/collision.obj
+  name: spatula
+  pose:
+    position:
+    - 0.3926656246185303
+    - -0.42459651827812195
+    - 0.013782436028122902
+    quaternion:
+    - 0.9900621771812439
+    - 0.014270812273025513
+    - -0.06286188960075378
+    - 0.12498646229505539
+  scale: 1.0
+  visual:
+    path: object/033_spatula/textured.obj
+- collision:
+    path: object/077_rubiks_cube/collision.obj
+  name: rubiks_cube
+  pose:
+    position:
+    - 0.14866474270820618
+    - -0.2968483567237854
+    - 0.014688600786030293
+    quaternion:
+    - 0.7610001564025879
+    - -0.002745300531387329
+    - -5.092844367027283e-05
+    - 0.6487459540367126
+  scale: 1.0
+  visual:
+    path: object/077_rubiks_cube/textured.obj
+- collision:
+    path: object/004_sugar_box/collision.obj
+  name: sugar_box
+  pose:
+    position:
+    - 0.5155602097511292
+    - -0.23539048433303833
+    - 0.35940077900886536
+    quaternion:
+    - 0.8059879541397095
+    - -0.015646949410438538
+    - -0.0027588012162595987
+    - -0.5917189121246338
+  scale: 1.0
+  visual:
+    path: object/004_sugar_box/textured.obj
+- collision:
+    path: object/003_cracker_box/collision.obj
+  name: cracker_box
+  pose:
+    position:
+    - 0.13388043642044067
+    - -0.1681717485189438
+    - 0.16345074772834778
+    quaternion:
+    - 0.26555028557777405
+    - 0.8669848442077637
+    - -0.41973358392715454
+    - 0.040548522025346756
+  scale: 1.0
+  visual:
+    path: object/003_cracker_box/textured.obj
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/1_scene_config.yaml b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/1_scene_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fa1998cdc59ecda1435d93907526b4a87bcb4ac1
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/1_scene_config.yaml
@@ -0,0 +1,458 @@
+objects:
+- collision:
+    path: object/table/desktable.obj
+  name: table
+  pose:
+    position:
+    - 0.4000000059604645
+    - 0.0
+    - -0.75
+    quaternion:
+    - 0.0
+    - 0.0
+    - 0.7071068286895752
+    - 0.7071068286895752
+  scale:
+  - 2.0
+  - 1.0
+  - 1.5
+  visual:
+    path: object/table/desktable.glb
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_01
+  pose:
+    position:
+    - 0.17368100583553314
+    - 0.32591354846954346
+    - 0.19764573872089386
+    quaternion:
+    - 0.9584034085273743
+    - 4.656612873077393e-10
+    - 8.321876521222293e-11
+    - -0.2854175269603729
+  scale:
+  - 0.43156745678016356
+  - 0.4938321481829596
+  - 1.0544241940054395
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_02
+  pose:
+    position:
+    - 0.4344547390937805
+    - 0.02579832822084427
+    - 0.12995675206184387
+    quaternion:
+    - 0.9969521164894104
+    - 4.656612873077393e-10
+    - 5.85168891120702e-09
+    - -0.07801619917154312
+  scale:
+  - 0.40738405359814406
+  - 0.5761527279656022
+  - 0.7179754719214234
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/051_large_clamp/collision.obj
+  name: large_clamp
+  pose:
+    position:
+    - 0.5183185935020447
+    - 0.316253125667572
+    - 0.00531975831836462
+    quaternion:
+    - 0.8272925019264221
+    - 0.028668347746133804
+    - 0.004300741013139486
+    - -0.5610230565071106
+  scale: 1.0
+  visual:
+    path: object/051_large_clamp/textured.obj
+- collision:
+    path: object/032_knife/collision.obj
+  name: knife
+  pose:
+    position:
+    - 0.14780382812023163
+    - -0.4084206521511078
+    - -0.006699821911752224
+    quaternion:
+    - 0.9292344450950623
+    - -0.05428450554609299
+    - -0.020429210737347603
+    - 0.3649100959300995
+  scale: 1.0
+  visual:
+    path: object/032_knife/textured.obj
+- collision:
+    path: object/038_padlock/collision.obj
+  name: padlock
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+- collision:
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+  name: sugar_box
+  pose:
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+  visual:
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+- collision:
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+  name: cracker_box
+  pose:
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+  visual:
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/3_scene_config.yaml b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/3_scene_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..da526a5e23280ed99a82e9030c6bc116d5160e8a
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/3_scene_config.yaml
@@ -0,0 +1,458 @@
+objects:
+- collision:
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+  name: table
+  pose:
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+  scale:
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+  visual:
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+- collision:
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+  name: box_01
+  pose:
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+  visual:
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+- collision:
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+  name: box_02
+  pose:
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+- collision:
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+    - 0.5924507975578308
+  scale: 1.0
+  visual:
+    path: object/015_peach/textured.obj
+- collision:
+    path: object/014_lemon/collision.obj
+  name: lemon
+  pose:
+    position:
+    - 0.6887364983558655
+    - -0.27862852811813354
+    - 0.012240268290042877
+    quaternion:
+    - 0.7730244398117065
+    - 5.4366886615753174e-05
+    - 3.4049153327941895e-05
+    - 0.6343764066696167
+  scale: 1.0
+  visual:
+    path: object/014_lemon/textured.obj
+- collision:
+    path: object/055_baseball/collision.obj
+  name: baseball
+  pose:
+    position:
+    - 0.09349945932626724
+    - 0.22306804358959198
+    - 0.02174980938434601
+    quaternion:
+    - -0.683356761932373
+    - -0.19521087408065796
+    - -0.5357841849327087
+    - 0.45590776205062866
+  scale: 1.0
+  visual:
+    path: object/055_baseball/textured.obj
+- collision:
+    path: object/026_sponge/collision.obj
+  name: sponge
+  pose:
+    position:
+    - 0.3988693952560425
+    - -0.34438300132751465
+    - -0.004402717109769583
+    quaternion:
+    - 0.7330365180969238
+    - -0.0001355656422674656
+    - -0.0130578288808465
+    - -0.6800640225410461
+  scale: 1.0
+  visual:
+    path: object/026_sponge/textured.obj
+- collision:
+    path: object/007_tuna_fish_can/collision.obj
+  name: tuna_fish_can
+  pose:
+    position:
+    - 0.27370795607566833
+    - -0.1388637125492096
+    - 0.0028000662568956614
+    quaternion:
+    - 0.9668454527854919
+    - -0.0014312444254755974
+    - 0.0013954655732959509
+    - 0.25535446405410767
+  scale: 1.0
+  visual:
+    path: object/007_tuna_fish_can/textured.obj
+- collision:
+    path: object/017_orange/collision.obj
+  name: orange
+  pose:
+    position:
+    - 0.4011644124984741
+    - -0.47028490900993347
+    - 0.02165462262928486
+    quaternion:
+    - 0.8645733594894409
+    - 0.023776907473802567
+    - 0.04256200045347214
+    - 0.5001361966133118
+  scale: 1.0
+  visual:
+    path: object/017_orange/textured.obj
+- collision:
+    path: object/033_spatula/collision.obj
+  name: spatula
+  pose:
+    position:
+    - 0.1743464320898056
+    - 0.037704549729824066
+    - 0.002662036335095763
+    quaternion:
+    - 0.9426295757293701
+    - 4.877219907939434e-06
+    - -9.91440174402669e-07
+    - -0.3338407874107361
+  scale: 1.0
+  visual:
+    path: object/033_spatula/textured.obj
+- collision:
+    path: object/077_rubiks_cube/collision.obj
+  name: rubiks_cube
+  pose:
+    position:
+    - 0.0694955438375473
+    - -0.45188552141189575
+    - 0.01468676794320345
+    quaternion:
+    - 0.7302193641662598
+    - -0.0027396678924560547
+    - -0.00017812475562095642
+    - 0.6832074522972107
+  scale: 1.0
+  visual:
+    path: object/077_rubiks_cube/textured.obj
+- collision:
+    path: object/004_sugar_box/collision.obj
+  name: sugar_box
+  pose:
+    position:
+    - 0.6818307638168335
+    - -0.18519429862499237
+    - 0.07367145270109177
+    quaternion:
+    - 0.9935358762741089
+    - -0.006377739831805229
+    - -0.01060091145336628
+    - -0.11284325271844864
+  scale: 1.0
+  visual:
+    path: object/004_sugar_box/textured.obj
+- collision:
+    path: object/003_cracker_box/collision.obj
+  name: cracker_box
+  pose:
+    position:
+    - 0.18190571665763855
+    - 0.4329392910003662
+    - 0.06135351210832596
+    quaternion:
+    - 0.7125244736671448
+    - -0.4413362741470337
+    - 0.5452485680580139
+    - -0.015334106050431728
+  scale: 1.0
+  visual:
+    path: object/003_cracker_box/textured.obj
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/9_scene_config.yaml b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/9_scene_config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8a0cbe5a64d1326d68e387edff71def4bf34e117
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/env_config/distractorbox/9_scene_config.yaml
@@ -0,0 +1,458 @@
+objects:
+- collision:
+    path: object/table/desktable.obj
+  name: table
+  pose:
+    position:
+    - 0.4000000059604645
+    - 0.0
+    - -0.75
+    quaternion:
+    - 0.0
+    - 0.0
+    - 0.7071068286895752
+    - 0.7071068286895752
+  scale:
+  - 2.0
+  - 1.0
+  - 1.5
+  visual:
+    path: object/table/desktable.glb
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_01
+  pose:
+    position:
+    - 0.22288964688777924
+    - 0.29875046014785767
+    - 0.19764569401741028
+    quaternion:
+    - 0.9955460429191589
+    - -1.1641532182693481e-10
+    - 1.4283614291343838e-09
+    - -0.09427783638238907
+  scale:
+  - 0.43156745678016356
+  - 0.4938321481829596
+  - 1.0544241940054395
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/box_01/box_01.gltf
+  name: box_02
+  pose:
+    position:
+    - 0.47560828924179077
+    - 0.011828921735286713
+    - 0.12995871901512146
+    quaternion:
+    - 0.9665859937667847
+    - 0.0
+    - -7.548806024715304e-11
+    - 0.2563430070877075
+  scale:
+  - 0.40738405359814406
+  - 0.5761527279656022
+  - 0.7179754719214234
+  visual:
+    path: object/box_01/box_01.gltf
+- collision:
+    path: object/051_large_clamp/collision.obj
+  name: large_clamp
+  pose:
+    position:
+    - 0.35688209533691406
+    - -0.10166856646537781
+    - 0.004791799001395702
+    quaternion:
+    - -0.00013015689910389483
+    - 0.7272769808769226
+    - -0.686330258846283
+    - -0.004357784055173397
+  scale: 1.0
+  visual:
+    path: object/051_large_clamp/textured.obj
+- collision:
+    path: object/032_knife/collision.obj
+  name: knife
+  pose:
+    position:
+    - 0.4620906114578247
+    - 0.06777108460664749
+    - 0.2777107357978821
+    quaternion:
+    - -0.1424867957830429
+    - 0.9358080625534058
+    - 0.31943368911743164
+    - -0.0438537523150444
+  scale: 1.0
+  visual:
+    path: object/032_knife/textured.obj
+- collision:
+    path: object/038_padlock/collision.obj
+  name: padlock
+  pose:
+    position:
+    - -0.005847564432770014
+    - 0.29753240942955017
+    - 0.0007152530597522855
+    quaternion:
+    - -0.14081990718841553
+    - 0.0737982839345932
+    - -0.005819572135806084
+    - -0.9872638583183289
+  scale: 1.0
+  visual:
+    path: object/038_padlock/textured.obj
+- collision:
+    path: object/005_tomato_soup_can/collision.obj
+  name: tomato_soup_can
+  pose:
+    position:
+    - 0.6511421203613281
+    - -0.2821510136127472
+    - 0.03674251586198807
+    quaternion:
+    - 0.9023494720458984
+    - 0.004053411539644003
+    - -0.002442792057991028
+    - -0.4309794306755066
+  scale: 1.0
+  visual:
+    path: object/005_tomato_soup_can/textured.obj
+- collision:
+    path: object/057_racquetball/collision.obj
+  name: racquetball
+  pose:
+    position:
+    - 0.33836066722869873
+    - 0.21845658123493195
+    - 0.01335947122424841
+    quaternion:
+    - 0.9792991280555725
+    - 0.0699000209569931
+    - -0.06793676316738129
+    - 0.1774032711982727
+  scale: 1.0
+  visual:
+    path: object/057_racquetball/textured.obj
+- collision:
+    path: object/054_softball/collision.obj
+  name: softball
+  pose:
+    position:
+    - 0.14509090781211853
+    - -0.3677173852920532
+    - 0.03274163603782654
+    quaternion:
+    - 0.894152045249939
+    - 0.03941861540079117
+    - -0.010465703904628754
+    - 0.44590234756469727
+  scale: 1.0
+  visual:
+    path: object/054_softball/textured.obj
+- collision:
+    path: object/009_gelatin_box/collision.obj
+  name: gelatin_box
+  pose:
+    position:
+    - 0.09230717271566391
+    - -0.07540824264287949
+    - 0.03075527399778366
+    quaternion:
+    - 0.7210660576820374
+    - -0.6638611555099487
+    - -0.1324901431798935
+    - 0.1476428061723709
+  scale: 1.0
+  visual:
+    path: object/009_gelatin_box/textured.obj
+- collision:
+    path: object/011_banana/collision.obj
+  name: banana
+  pose:
+    position:
+    - 0.25274768471717834
+    - 0.5630350708961487
+    - 0.004437322728335857
+    quaternion:
+    - -0.26365020871162415
+    - -0.002831563353538513
+    - 0.0018312595784664154
+    - -0.964612603187561
+  scale: 1.0
+  visual:
+    path: object/011_banana/textured.obj
+- collision:
+    path: object/043_phillips_screwdriver/collision.obj
+  name: phillips_screwdriver
+  pose:
+    position:
+    - 0.08685889095067978
+    - -0.2411920726299286
+    - 0.000890759751200676
+    quaternion:
+    - 0.30528751015663147
+    - -0.6414585113525391
+    - -0.648400604724884
+    - 0.2736915647983551
+  scale: 1.0
+  visual:
+    path: object/043_phillips_screwdriver/textured.obj
+- collision:
+    path: object/073-a_lego_duplo/collision.obj
+  name: lego_duplo
+  pose:
+    position:
+    - 0.36021295189857483
+    - -0.3918026387691498
+    - 0.0019375779666006565
+    quaternion:
+    - 0.7011419534683228
+    - 0.7011470198631287
+    - -0.09163631498813629
+    - -0.09162930399179459
+  scale: 1.0
+  visual:
+    path: object/073-a_lego_duplo/textured.obj
+- collision:
+    path: object/006_mustard_bottle/collision.obj
+  name: mustard_bottle
+  pose:
+    position:
+    - 0.5751137137413025
+    - -0.1801433116197586
+    - 0.08319046348333359
+    quaternion:
+    - 0.8944754600524902
+    - -0.0030710180290043354
+    - 0.0014999208506196737
+    - -0.4471040964126587
+  scale: 1.0
+  visual:
+    path: object/006_mustard_bottle/textured.obj
+- collision:
+    path: object/002_master_chef_can/collision.obj
+  name: master_chef_can
+  pose:
+    position:
+    - 0.571797788143158
+    - -0.07740773260593414
+    - 0.056463778018951416
+    quaternion:
+    - 0.9720659852027893
+    - 3.0583760235458612e-06
+    - -2.06657568924129e-06
+    - 0.2347080260515213
+  scale: 1.0
+  visual:
+    path: object/002_master_chef_can/textured.obj
+- collision:
+    path: object/036_wood_block/collision.obj
+  name: wood_block
+  pose:
+    position:
+    - 0.49862009286880493
+    - 0.32725298404693604
+    - 0.03162414953112602
+    quaternion:
+    - 0.7047209739685059
+    - -0.7035831809043884
+    - -0.06195170432329178
+    - 0.06709162890911102
+  scale: 1.0
+  visual:
+    path: object/036_wood_block/textured.obj
+- collision:
+    path: object/012_strawberry/collision.obj
+  name: strawberry
+  pose:
+    position:
+    - 0.44274061918258667
+    - -0.38756731152534485
+    - 0.008256493136286736
+    quaternion:
+    - 0.8039378523826599
+    - -0.07072664052248001
+    - 0.022730957716703415
+    - -0.5900551080703735
+  scale: 1.0
+  visual:
+    path: object/012_strawberry/textured.obj
+- collision:
+    path: object/072-c_toy_airplane/collision.obj
+  name: toy_airplane
+  pose:
+    position:
+    - 0.25686347484588623
+    - 0.11407414078712463
+    - 0.019497955217957497
+    quaternion:
+    - 0.8170802593231201
+    - -5.409816367318854e-06
+    - -1.6311612853314728e-05
+    - 0.5765240788459778
+  scale: 1.0
+  visual:
+    path: object/072-c_toy_airplane/textured.obj
+- collision:
+    path: object/015_peach/collision.obj
+  name: peach
+  pose:
+    position:
+    - 0.16602928936481476
+    - 0.13869813084602356
+    - 0.014315558597445488
+    quaternion:
+    - 0.8885988593101501
+    - 0.09681116044521332
+    - -0.22163920104503632
+    - 0.389738529920578
+  scale: 1.0
+  visual:
+    path: object/015_peach/textured.obj
+- collision:
+    path: object/014_lemon/collision.obj
+  name: lemon
+  pose:
+    position:
+    - 0.6803424954414368
+    - -0.4778844714164734
+    - 0.012237905524671078
+    quaternion:
+    - 0.9628485441207886
+    - 6.604939699172974e-05
+    - 5.751848220825195e-06
+    - 0.27004218101501465
+  scale: 1.0
+  visual:
+    path: object/014_lemon/textured.obj
+- collision:
+    path: object/055_baseball/collision.obj
+  name: baseball
+  pose:
+    position:
+    - -0.36434707045555115
+    - 0.48415064811706543
+    - -0.7640279531478882
+    quaternion:
+    - -0.4256981313228607
+    - 0.39007070660591125
+    - -0.7582579851150513
+    - 0.3027718663215637
+  scale: 1.0
+  visual:
+    path: object/055_baseball/textured.obj
+- collision:
+    path: object/026_sponge/collision.obj
+  name: sponge
+  pose:
+    position:
+    - 0.08433457463979721
+    - 0.16534829139709473
+    - -0.004406681749969721
+    quaternion:
+    - 0.9494009017944336
+    - 0.005306074395775795
+    - -0.011939648538827896
+    - -0.3137950599193573
+  scale: 1.0
+  visual:
+    path: object/026_sponge/textured.obj
+- collision:
+    path: object/007_tuna_fish_can/collision.obj
+  name: tuna_fish_can
+  pose:
+    position:
+    - 0.23340903222560883
+    - 0.40106886625289917
+    - 0.4234093725681305
+    quaternion:
+    - 0.6285783052444458
+    - 8.878970402292907e-05
+    - 0.006439802702516317
+    - 0.7777197360992432
+  scale: 1.0
+  visual:
+    path: object/007_tuna_fish_can/textured.obj
+- collision:
+    path: object/017_orange/collision.obj
+  name: orange
+  pose:
+    position:
+    - 0.29783713817596436
+    - -0.2825593948364258
+    - 0.02425583079457283
+    quaternion:
+    - 0.7831382751464844
+    - 0.2090703248977661
+    - -0.06132107973098755
+    - -0.5824294090270996
+  scale: 1.0
+  visual:
+    path: object/017_orange/textured.obj
+- collision:
+    path: object/033_spatula/collision.obj
+  name: spatula
+  pose:
+    position:
+    - 0.49281230568885803
+    - -0.29754599928855896
+    - 0.0026670354418456554
+    quaternion:
+    - 0.9126969575881958
+    - 6.940099410712719e-06
+    - 1.8855025700759143e-06
+    - -0.40863731503486633
+  scale: 1.0
+  visual:
+    path: object/033_spatula/textured.obj
+- collision:
+    path: object/077_rubiks_cube/collision.obj
+  name: rubiks_cube
+  pose:
+    position:
+    - 0.683811366558075
+    - 0.07209564745426178
+    - 0.014685331843793392
+    quaternion:
+    - 0.7167507410049438
+    - -0.0027378499507904053
+    - -0.00022997334599494934
+    - 0.697324275970459
+  scale: 1.0
+  visual:
+    path: object/077_rubiks_cube/textured.obj
+- collision:
+    path: object/004_sugar_box/collision.obj
+  name: sugar_box
+  pose:
+    position:
+    - 0.6434389352798462
+    - 0.32049962878227234
+    - 0.07367409020662308
+    quaternion:
+    - 0.9948025941848755
+    - -0.006252215709537268
+    - -0.010675374418497086
+    - -0.10106871277093887
+  scale: 1.0
+  visual:
+    path: object/004_sugar_box/textured.obj
+- collision:
+    path: object/003_cracker_box/collision.obj
+  name: cracker_box
+  pose:
+    position:
+    - 0.26133647561073303
+    - -0.35625046491622925
+    - 0.06820447742938995
+    quaternion:
+    - 0.2144145369529724
+    - -0.23371748626232147
+    - 0.6663254499435425
+    - -0.674842894077301
+  scale: 1.0
+  visual:
+    path: object/003_cracker_box/textured.obj
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..08b71c4f56ff77a0d2d0e548071ce5763c71f7f9
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__init__.py
@@ -0,0 +1 @@
+from . import scene_generator_env
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/__init__.cpython-310.pyc b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/__init__.cpython-310.pyc
new file mode 100644
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/empty_random_env.cpython-310.pyc b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/empty_random_env.cpython-310.pyc
new file mode 100644
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/grasp_bowl_v1.cpython-310.pyc b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/__pycache__/grasp_bowl_v1.cpython-310.pyc
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/clutter_scene_generator.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/clutter_scene_generator.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ae88affd590de62b6c5ef9ac8278f980c131ac3
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/clutter_scene_generator.py
@@ -0,0 +1,18 @@
+import os.path as osp
+from pathlib import Path
+from typing import List
+
+import numpy as np
+import sapien
+import sapien.render
+import torch
+from transforms3d.euler import euler2quat
+
+from mani_skill.agents.multi_agent import MultiAgent
+from mani_skill.agents.robots.fetch import FETCH_WHEELS_COLLISION_BIT
+from mani_skill.utils.building.ground import build_ground
+from mani_skill.utils.scene_builder import SceneBuilder
+
+
+# Generate clutter scene from ycb objects
+# save the clutter scene to yaml config files
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/empty_random_env.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/empty_random_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..cccb0d020d540c90edfa5a1fe86711efedb34bbb
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/empty_random_env.py
@@ -0,0 +1,235 @@
+from typing import Any, Dict, Union
+
+import numpy as np
+import sapien
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+
+from mani_skill.agents.robots import Panda
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.structs.types import Array
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from .scene_loader_builder import SceneLoaderBuilder
+
+from msx_envs.agent import PandaSphere
+
+
+@register_env("EmptyRandom-v0", max_episode_steps=500)
+class EmptyRandomEnv(BaseEnv):
+    SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+    agent: Union[Panda, PandaSphere]
+
+    def __init__(self, object_config_dir="base_configs/base_table.yaml", 
+                        create_obstacle_point_cloud=True, is_inference=False, rand_level = None, **kwargs):    
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        super().__init__(**kwargs)
+    
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params__()
+
+    def _load_scene(self, options: dict):
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        max_iter = 10
+        for i in range(max_iter):
+            self._init_env_objects()
+            try:
+                self.table_scene.build()
+                print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+
+    def _build_sphere_site(self, radius, color=(0, 1, 0), name="goal_site"):
+        """Build a sphere site (visual only). Used to indicate goal position."""
+        builder = self.scene.create_actor_builder()
+        builder.add_sphere_visual(radius=radius, material=sapien.render.RenderMaterial(base_color=[0.8, 0.4, 0.3, 0.5]))
+        sphere = builder.build_static(name)
+        return sphere
+
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+        
+    def _init_env_objects(self):
+        self.goal_site = self._build_sphere_site(radius=0.01)
+        # # ---------------Initialize goal pose for task ---------------#
+        # # TODO: call inverse kinematics to set goal pose
+        # initial_qpos = np.array([0.20, 1.49, -1.78, -1.42, 0.50, 2.74, 1.77, 0.05, 0.05])
+        # joint_states = None
+        # # Sample a goal position far enough from robot start point
+        # while joint_states is None:
+        #     for i in range(15):
+        #         goal_x = self._episode_rng.uniform(0.2, 0.7)
+        #         goal_y = self._episode_rng.uniform(-0.65, 0.65)
+        #         goal_z = self._episode_rng.uniform(0.05, 0.60)
+        #         goal_pos = np.hstack([goal_x, goal_y, goal_z])
+        #         tcp_p = self.agent.tcp.pose.p
+        #         if isinstance(tcp_p, torch.Tensor):
+        #             tcp_p = tcp_p.detach().cpu().numpy()
+        #         if np.linalg.norm(goal_pos - tcp_p) > 0.3:
+        #             print(f"Found a valid goal at {i}-th trial")
+        #             break
+        #     base_quat = np.array([0.5, 0.5, -0.5, 0.5])  # Your original quaternion
+        #     goal_pose = sapien.Pose(goal_pos, base_quat)
+        #     found_valid_start, joint_states = self.table_scene._check_robot_pose_valid(goal_pose, initial_qpos)
+
+        #     if joint_states is not None:
+        #         self.goal_site.set_pose(sapien.Pose(goal_pos))
+
+        #         goal_pose_tensor = torch.tensor(goal_pose.p, device=self.agent.tcp.pose.raw_pose.device)
+        #         base_quat_tensor = torch.tensor(base_quat, device=self.agent.tcp.pose.raw_pose.device)
+        #         self.goal_pose = Pose.create_from_pq(p=goal_pose_tensor, q=base_quat_tensor)
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        succeed = self._initialize_episode_task()
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            succeed = succeed and self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+        print("----- Initialize new episode succeed {} ------".format(succeed))
+        return succeed
+
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+            return True
+        return False
+
+    def _initialize_episode_task(self):
+        # ---- Initialize goal pose for task -----#
+        initial_qpos = np.array([0.20, 1.49, -1.78, -1.42, 0.50, 2.74, 1.77, 0.05, 0.05])
+        joint_states = None
+        # Sample a goal position far enough from robot start point
+        while joint_states is None:
+            for i in range(15):
+                goal_x = self._episode_rng.uniform(0.2, 0.65)
+                goal_y = self._episode_rng.uniform(-0.55, 0.55)
+                goal_z = self._episode_rng.uniform(0.05, 0.60)
+                goal_pos = np.hstack([goal_x, goal_y, goal_z])
+                tcp_p = self.agent.tcp.pose.p
+                if isinstance(tcp_p, torch.Tensor):
+                    tcp_p = tcp_p.detach().cpu().numpy()
+                if np.linalg.norm(goal_pos - tcp_p) > 0.4:
+                    print(f"Found a valid goal at {i}-th trial")
+                    break
+
+            # base_quat = np.array([0.5, 0.5, -0.5, 0.5])  # For shelf environment
+            base_quat = np.array([-0.5, -0.5, -0.5, 0.5])  # For cup grasp
+            # base_quat = np.array([0, 1, 0, 0])  # original quaternion
+            base_quat = self._generate_perturbed_quaternion(base_quat)
+            print("base_quat: ", base_quat)
+            goal_pose = sapien.Pose(goal_pos, base_quat)
+        
+            found_valid_start, joint_states = self.table_scene._check_robot_pose_valid(
+                robot_pose=goal_pose,
+                initial_qpos=initial_qpos,
+                current_qpos=self.agent.robot.get_qpos()
+            )
+
+            if joint_states is not None:
+                self.goal_site.set_pose(sapien.Pose(goal_pos))
+                goal_pose_tensor = torch.tensor(goal_pose.p, device=self.agent.tcp.pose.raw_pose.device)
+                base_quat_tensor = torch.tensor(base_quat, device=self.agent.tcp.pose.raw_pose.device)
+                self.goal_pose = Pose.create_from_pq(p=goal_pose_tensor, q=base_quat_tensor)
+                return True
+        return False
+
+    @property
+    def _default_human_render_camera_configs(self):
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            pose = look_at([1.4, 0.2, 0.5], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+    
+    def evaluate(self):
+        # current tcp pose
+        tcp_p = self.agent.tcp.pose.p
+        goal_p = self.goal_pose.p 
+        dist = torch.norm(tcp_p - goal_p, p=2)
+        is_reach = dist < 0.05
+        return {
+            "success": torch.as_tensor([is_reach], device=self.device)
+        }
+    
+    def _get_obs_extra(self, info: Dict):
+        goal_pose = self.goal_pose
+        # return extra observation
+        obs = dict(
+            tcp_pose=self.agent.tcp.pose.raw_pose.detach().cpu().numpy(),
+            goal_pose=goal_pose.raw_pose.detach().cpu().numpy(),
+            goal_pos=goal_pose.p.detach().cpu().numpy(),
+        )
+        return obs
+    
+    def compute_dense_reward(self, obs: Any, action: Array, info: Dict):
+        reward = torch.tensor([1.0], device=self.device, dtype=torch.float32)
+        return reward
+    
+    def _generate_perturbed_quaternion(self, base_quat, max_angle_deviation=0.5):
+        """Generate a quaternion that's slightly different from the base quaternion.
+        
+        Args:
+            base_quat: Base quaternion [w, x, y, z]
+            max_angle_deviation: Maximum angle deviation in radians (default: 0.5 ≈ 29 degrees)
+        
+        Returns:
+            Perturbed quaternion as numpy array
+        """
+        # Ensure base_quat is properly formatted and normalized
+        if len(base_quat) != 4:
+            base_quat = np.array([1., 0., 0., 0.])
+        else:
+            base_quat = base_quat / np.linalg.norm(base_quat)
+        
+        # Generate smaller random rotation
+        angle = self._episode_rng.uniform(-max_angle_deviation, max_angle_deviation)
+        
+        # Generate random axis biased towards the original orientation
+        random_axis = self._episode_rng.normal(0, 0.1, 3)  # Smaller standard deviation
+        random_axis = random_axis / np.linalg.norm(random_axis)
+        
+        # Create quaternion for small rotation
+        s = np.cos(angle/2)
+        v = random_axis * np.sin(angle/2)
+        delta_quat = np.array([s, v[0], v[1], v[2]])
+        
+        # Multiply quaternions (quaternion composition)
+        w1, x1, y1, z1 = base_quat
+        w2, x2, y2, z2 = delta_quat
+        result_quat = np.array([
+            w1*w2 - x1*x2 - y1*y2 - z1*z2,
+            w1*x2 + x1*w2 + y1*z2 - z1*y2,
+            w1*y2 - x1*z2 + y1*w2 + z1*x2,
+            w1*z2 + x1*y2 - y1*x2 + z1*w2
+        ])
+        
+        # Normalize the resulting quaternion
+        return result_quat / np.linalg.norm(result_quat)
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_bowl_v1.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_bowl_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..99f1e9357b21ee698114c9855747eba2baa02b06
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_bowl_v1.py
@@ -0,0 +1,301 @@
+from typing import Any, Dict, Union
+
+import os
+import numpy as np
+import sapien
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+
+from mani_skill.envs.tasks.tabletop.grasp_bowl_v0 import GraspBowlEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.building import actors
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from mani_skill.examples.motionplanning.noahbiarm.utils import get_3d_bbox, get_actor_obb
+from mani_skill.agents.robots import Panda
+
+from msx_envs.agent import PandaSphere
+from .scene_loader_builder import SceneLoaderBuilder
+
+
+@register_env("GraspBowl-v1", max_episode_steps=500)
+class GraspBowlV1Env(GraspBowlEnv):
+    def __init__(self, object_config_dir="base_configs/base_table.yaml", 
+                        create_obstacle_point_cloud=True, is_inference=False, **kwargs):
+        SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+        self.agent: Union[Panda, PandaSphere]
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        super().__init__(**kwargs)
+
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params()
+        # Add any additional initialization for GraspBowlV1Env here
+    
+    def _get_obs_extra(self, info: Dict):
+        # check for grasping
+        self.grasped = max(self.grasped , self.agent.is_grasping(self.bowl))
+        # print("!!current grasped self.agent.is_grasping(self.bowl) is: ", self.agent.is_grasping(self.bowl))
+
+        if not self.grasped:
+            goal_pose = self.compute_grasp_pose()
+        else:
+            goal_pose = self.compute_release_pose()
+
+        if not self.is_inference:
+            # return extra observation, for training and data collection
+            obs = dict( #data collection setting
+                tcp_pose=self.agent.tcp.pose.raw_pose.detach().cpu().numpy(),
+                goal_pose=goal_pose.raw_pose.detach().cpu().numpy(),
+                goal_pos=goal_pose.p.detach().cpu().numpy(),
+                grasped=self.grasped,
+            )
+        else:
+            obs = dict( # inference setting
+                tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pose = goal_pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pos  = goal_pose.p.detach().cpu().numpy().squeeze(0),
+                grasped=self.grasped,
+            )
+        if self.create_obstacle_point_cloud:
+            # print("obstacle_point_cloud is: ", self.obstacle_point_cloud.shape)
+            obs.update({
+                'obstacle_point_cloud': self.table_scene.obstacle_point_cloud
+            })
+        return obs
+    
+    def _load_scene(self, options: dict):
+        print("mesh dir: ", self.mesh_dir)
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        max_iter = 10
+        for i in range(max_iter):
+            self._init_env_objects()  # init bowls
+            try:
+                self.table_scene.build()
+                print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+    
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+    
+    def evaluate(self):
+        BOWL_D = self.bowl_extents[0]
+        goal_extents = torch.from_numpy(self.goal_extents.copy())
+        goal_p, _ = self.get_goal_site_pose()
+        is_bowl_placed = (
+            self.is_inside(self.bowl.pose.p, goal_p , goal_extents)
+        )
+
+        euler = rotation_conversions.quaternion_to_euler(self.bowl.pose.q)
+        is_bowl_vertical = (
+            ((torch.abs(torch.abs(euler[:, 1])) - np.pi/2) <= np.pi/4 )&
+            ((torch.abs(torch.abs(euler[:, 2])) - np.pi/2) <= np.pi/4 )
+        ) 
+
+        # tcp pose
+        tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy()
+        if self.tcp_pose_valid and ( (tcp_pose[:, 0] < -0.2).all() or (tcp_pose[:, 2] > 0.65).all()):
+            print("tcp pose is out of boundary!, x axis is: ", tcp_pose[:, 0])
+            self.tcp_pose_valid = False
+
+        not_grasping = torch.logical_not(self.agent.is_grasping(self.bowl))
+
+        # plate is static
+        is_bowl_static = self.bowl.is_static()
+        
+        tcp_pose_valid_tensor = torch.tensor(self.tcp_pose_valid, device=is_bowl_placed.device)
+        success = torch.logical_and(is_bowl_placed, tcp_pose_valid_tensor)
+
+        return {
+            "success": success
+        }
+
+    
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        print("-----Initialize episode panda------")
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+
+            if self.rand_level >= 5:
+                side = torch.randint(0, 2, (b, 1)) * 2 - 1
+            else:
+                side = torch.ones((b, 1))
+
+            ##################################################################################
+            # fork pose initialize
+            x_range = 0.15
+            y_range = 0.15
+
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.25
+            y = -1 * side * torch.ones((b, 1)) * 0.55
+            z = torch.zeros((b, 1))
+            # default rotation
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 0] = np.pi/2 
+            
+            # ---- Apply randomness by level ----
+            # default no jitter
+            device = self.device if hasattr(self, "device") else torch.device("cpu")
+            dtype = torch.float32
+            x_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+            y_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                roll_jit = torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+
+           # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b,3)
+            p = robot_p + xyz                   # (b,3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b,4)  (check expected euler order)
+            self.bowl.set_pose(Pose.create_from_pq(p=p, q=q))
+
+            ##################################################################################
+            # rack pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.25
+            y = 1 * side * torch.ones((b, 1)) * 0.55
+            z = torch.zeros((b, 1))
+
+            # default rotation (keep ZYX comment consistent with your conversion)
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 0] = np.pi  # roll
+
+            # default no jitter
+            x_rand = torch.zeros((b, 1), dtype=torch.float32)
+            y_rand = torch.zeros((b, 1), dtype=torch.float32)
+
+            # ---- Apply randomness by level ----
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                # rotation jitter around roll (optionally add yaw jitter if needed)
+                roll_jit =  torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+                # Optional:
+                # euler[:, 2] = euler[:, 2] + torch.empty((b,), dtype=dtype, device=device).uniform_(-np.pi/12, np.pi/12)
+
+            # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b, 3)
+            p = robot_p + xyz                   # (b, 3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b, 4)
+                        
+            self.rack.set_pose(Pose.create_from_pq(p=p, q=q))
+            # gp, gq = self.get_goal_site_pose()
+            # self.goal_site.set_pose(Pose.create_from_pq(p=gp, q=gq))
+
+    def _init_env_objects(self):
+        """Initialize bowl, rack, and goal site actors in the scene."""
+        # --- Bowl ---
+        self.bowl = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/kalas_bowl.obj"),
+            scale=(1, 1, 1),
+            color=np.array([0, 0, 255, 255], dtype=np.float32) / 255,
+            name="bowl",
+            body_type="dynamic",
+            add_collision=True,
+            initial_pose=sapien.Pose(p=[0, 0, 0], q=[1, 0, 0, 0]),
+        )
+        self.bowl_extents = get_actor_obb(self.bowl).extents
+
+        # --- Rack ---
+        self.rack = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/stamling_rack.obj"),
+            scale=(1, 1, 1),
+            color=np.array([216, 215, 211, 255], dtype=np.float32) / 255,
+            name="rack",
+            body_type="dynamic",
+            initial_pose=sapien.Pose(p=[0, 0, 0]),
+        )
+        self.rack_extents = get_actor_obb(self.rack).extents
+
+        # --- Goal site ---
+        self.goal_site = actors.build_box(
+            self.scene,
+            half_sizes=[
+                self.rack_extents[0] / 2 / 4,
+                self.rack_extents[1] / 2 * 0.9,
+                self.rack_extents[2] / 2,
+            ],
+            color=[0, 1, 0, 0],
+            name="goal_site",
+            body_type="kinematic",
+            add_collision=False,
+            initial_pose=sapien.Pose(
+                p=[+self.rack_extents[0] * 9.5 / 14 / 2, 0, self.rack_extents[2] / 2]
+            ),
+        )
+        self.goal_extents = np.array(
+            [self.rack_extents[0] / 4, self.rack_extents[1] * 0.9, self.rack_extents[2]]
+        )
+    
+    @property
+    def _default_human_render_camera_configs(self):
+        # import pdb; pdb.set_trace()
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            pose = look_at([1.4, 0.2, 0.5], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        # import pdb; pdb.set_trace()
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_box_v0.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_box_v0.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbc372f172fde5d28bef521dfd454291cac86f1c
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_box_v0.py
@@ -0,0 +1,819 @@
+# Issue with actor_builder
+from typing import Any, Dict, Union
+
+import os
+import yaml
+import random
+import numpy as np
+import sapien
+from sapien.physx import PhysxMaterial
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+import pathlib
+
+from mani_skill.envs.tasks.tabletop.grasp_bowl_v0 import GraspBowlEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.building import actors
+from mani_skill.utils.geometry import rotation_conversions, get_axis_aligned_bbox_for_actor
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from mani_skill.examples.motionplanning.noahbiarm.utils import get_3d_bbox, get_actor_obb
+from mani_skill.agents.robots import Panda
+from mani_skill import format_path
+from cfdp.utils.object_placement_2 import PlacementBounds, ObjectBoundingBox, find_valid_position_2d
+
+from msx_envs.agent import PandaSphere
+from msx_envs import assets, ENV_CONFIG_DIR
+
+from .scene_loader_builder import SceneLoaderBuilder
+
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.structs.types import Array
+
+
+@register_env("GraspBox-v0", max_episode_steps=500)
+class GraspBoxEnv(GraspBowlEnv):
+    def __init__(
+        self, 
+        object_config_dir="base_configs/base_table_shelf.yaml", 
+        create_obstacle_point_cloud=True, 
+        is_inference=False, 
+        model_config="asset_tables.yaml",
+        asset_root=str(pathlib.Path(assets.__file__).parent),
+        distractor_count: int = 16,
+        **kwargs
+        ):
+
+        SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+        self.agent: Union[Panda, PandaSphere]
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        self.model_configs = model_config
+        self.asset_root = asset_root
+        self._distractor_count = distractor_count
+        self._object_place_bounds = None # Calculated later
+        self.obstacle_actors = {} # Obstacle actors in the scene
+        self.base_pc = None
+        self._object_place_bounds = [ # table workspace placement bounds for obstacles and box
+                [0.1, -0.2461],  # minimum x and y
+                [0.8, 0.6],   # maximum x and y
+                [2]              # always [2]
+            ]
+
+        with open(self._get_asset_path(self.model_configs), 'r') as f:
+            # print("Loading model config from: ", self._get_asset_path(self.model_configs))
+            self.model_db = yaml.safe_load(f)
+
+        super().__init__(**kwargs)
+
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params()
+        # Add any additional initialization for GraspBowlV1Env here
+    
+    def _get_obs_extra(self, info: Dict):
+        # add function here to estimate box pose at each time step
+        # TODO YINTAO
+        # cehck data/panda_wristcam/GraspBox-v0/motionplanning/sample.dc.32.h5
+        obs = dict(
+            box_pose = self.box.pose.raw_pose.detach().cpu().numpy()
+        )
+        return obs
+    
+    def _load_scene(self, options: dict):
+        # print("mesh dir: ", self.mesh_dir)
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        self.obstacle_actors.clear()
+        self.obstacle_names = []
+
+        max_iter = 10
+        for i in range(max_iter):
+            # self.table_scene.obstacle_point_cloud = None
+            self._init_env_objects()  # init cup, rack, and goal site
+            self._load_new_ycb_obstacles() # load obstacles
+            try:
+                self.table_scene.build()
+                self.base_pc = self.table_scene.obstacle_point_cloud
+                # print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                pass
+                # print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+    
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+    
+    def evaluate(self):
+        # BOWL_D = self.bowl_extents[0]
+        # goal_extents = torch.from_numpy(self.goal_extents.copy())
+        # goal_p, _ = self.get_goal_site_pose()
+        # is_bowl_placed = (
+        #     self.is_inside(self.bowl.pose.p, goal_p , goal_extents)
+        # )
+
+        # euler = rotation_conversions.quaternion_to_euler(self.bowl.pose.q)
+        # is_bowl_vertical = (
+        #     ((torch.abs(torch.abs(euler[:, 1])) - np.pi/2) <= np.pi/4 )&
+        #     ((torch.abs(torch.abs(euler[:, 2])) - np.pi/2) <= np.pi/4 )
+        # ) 
+
+        # # tcp pose
+        # tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy()
+        # if self.tcp_pose_valid and ( (tcp_pose[:, 0] < -0.2).all() or (tcp_pose[:, 2] > 0.65).all()):
+        #     print("tcp pose is out of boundary!, x axis is: ", tcp_pose[:, 0])
+        #     self.tcp_pose_valid = False
+
+        # not_grasping = torch.logical_not(self.agent.is_grasping(self.bowl))
+
+        # # plate is static
+        # is_bowl_static = self.bowl.is_static()
+        
+        # tcp_pose_valid_tensor = torch.tensor(self.tcp_pose_valid, device=is_bowl_placed.device)
+        # success = torch.logical_and(is_bowl_placed, tcp_pose_valid_tensor)
+
+        goal_extents = torch.from_numpy(self.goal_extents.copy())
+        goal_p = self.goal_site.pose.p
+        box_p = self.box.pose.p
+
+        is_box_placed = (
+            self.is_inside(box_p, goal_p , goal_extents)
+        )
+        not_grasping = torch.logical_not(self.agent.is_grasping(self.box))
+
+        success = torch.logical_and(is_box_placed, not_grasping)
+
+        return {
+            "success": success
+        }
+
+    
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        # print("-----Initialize episode panda------")
+        # self.rand_level = 2
+        # print(f"rand level is: {self.rand_level}")
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+
+            if self.rand_level >= 5:
+                side = torch.randint(0, 2, (b, 1)) * 2 - 1
+            else:
+                side = torch.ones((b, 1))
+
+            x_range = 0.15
+            y_range = 0.15
+            ##################################################################################
+        #     # cup pose initialize
+        #     # ---- Base position (relative) ----
+        #     # on shelf
+        #     # x = torch.ones((b, 1)) * 0.3
+        #     # y = -1 * side * torch.ones((b, 1)) * 0.7
+        #     # z = torch.ones((b, 1)) * 0.01
+        #     # behind table (out of scene)
+        #     x = torch.ones((b, 1)) * -1
+        #     y = -1 * side * torch.ones((b, 1)) * 0
+        #     z = torch.ones((b, 1)) * -0.5
+
+        #     # default rotation
+        #     euler = torch.zeros((b, 3), dtype=torch.float32)
+        #     euler[:, 1] = np.pi # set cup upright
+            
+        #     # ---- Apply randomness by level ----
+        #     # default no jitter
+        #     device = self.device if hasattr(self, "device") else torch.device("cpu")
+        #     dtype = torch.float32
+        #     x_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+        #     y_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+
+        #     if self.rand_level == 1:
+        #         # small XY jitter (half range)
+        #         x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+        #         y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+        #     elif self.rand_level >= 2:
+        #         # larger XY jitter (full range)
+        #         x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+        #         y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+            
+        #     x = x + x_rand
+        #     y = y + y_rand
+
+        #     if self.rand_level >= 3:
+        #         roll_jit = torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+        #         euler[:, 0] = euler[:, 0] + roll_jit
+
+        #    # ---- Compose absolute pose ----
+        #     xyz = torch.cat((x, y, z), dim=1)   # (b,3)
+        #     # xyz = torch.tensor([[0.3, -0.7, 0.0]])
+        #     p = robot_p + xyz                   # (b,3)
+        #     q = rotation_conversions.euler_to_quaternion(euler)  # (b,4)  (check expected euler order)
+        #     self.bowl.set_pose(Pose.create_from_pq(p=p, q=q))
+
+            ##################################################################################
+            # # rack pose initialize
+            # # ---- Base position (relative) ----
+            # # x = torch.ones((b, 1)) * 0.25
+            # # y = 1 * side * torch.ones((b, 1)) * 0.55
+            # # z = torch.zeros((b, 1))
+            # # behind table (out of scene)
+            # x = torch.ones((b, 1)) * -1
+            # y = 1 * side * torch.ones((b, 1)) * 0
+            # z = torch.zeros((b, 1)) * -0.5
+
+            # # default rotation (keep ZYX comment consistent with your conversion)
+            # euler = torch.zeros((b, 3), dtype=torch.float32)
+            # euler[:, 0] = np.pi  # roll
+
+            # # default no jitter
+            # x_rand = torch.zeros((b, 1), dtype=torch.float32)
+            # y_rand = torch.zeros((b, 1), dtype=torch.float32)
+
+            # # ---- Apply randomness by level ----
+            # if self.rand_level == 1:
+            #     # small XY jitter (half range)
+            #     x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+            #     y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            # elif self.rand_level >= 2:
+            #     # larger XY jitter (full range)
+            #     x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+            #     y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            # x = x + x_rand
+            # y = y + y_rand
+
+            # if self.rand_level >= 3:
+            #     # rotation jitter around roll (optionally add yaw jitter if needed)
+            #     roll_jit =  torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+            #     euler[:, 0] = euler[:, 0] + roll_jit
+            #     # Optional:
+            #     # euler[:, 2] = euler[:, 2] + torch.empty((b,), dtype=dtype, device=device).uniform_(-np.pi/12, np.pi/12)
+
+            # # ---- Compose absolute pose ----
+            # xyz = torch.cat((x, y, z), dim=1)   # (b, 3)
+            # p = robot_p + xyz                   # (b, 3)
+            # q = rotation_conversions.euler_to_quaternion(euler)  # (b, 4)
+                        
+            # self.rack.set_pose(Pose.create_from_pq(p=p, q=q))
+            # # gp, gq = self.get_goal_site_pose()
+            # # self.goal_site.set_pose(Pose.create_from_pq(p=gp, q=gq))
+
+
+            self._recreate_ycb_objects("6d_pose/box.yaml") # recreate box with random scale and placement
+            self._initialize_new_scene_obstacles() # set initial state of obstacles
+            for name in self.obstacle_actors.keys():
+                self._settle_actor(actor=self.obstacle_actors[name])
+            
+        
+            
+    def _init_env_objects(self):
+        # for actor in list(self.scene.actors.keys()):
+        #     if "bowl" in actor:
+        #         self.scene.remove_actor(self.scene.actors[actor])
+        # """Initialize cup, rack, and goal site actors in the scene."""
+        # # --- Cup (**Named as bowl**) ---
+        # self.bowl = actors.build_nonconvex_from_mesh(
+        #     self.scene,
+        #     mesh_path=os.path.join(self.mesh_dir, "./assets/plates/kalas_mug.obj"),
+        #     scale=(1, 1, 1),
+        #     color=np.array([0, 0, 255, 255], dtype=np.float32) / 255,
+        #     name="mug",
+        #     body_type="dynamic",
+        #     add_collision=True,
+        #     initial_pose=sapien.Pose(p=[0, 0, 0], q=[1, 0, 0, 0]),
+        #     use_texture=False,
+        # )
+        # self.bowl_extents = get_actor_obb(self.bowl).extents
+
+        # # --- Rack ---
+        # self.rack = actors.build_nonconvex_from_mesh(
+        #     self.scene,
+        #     mesh_path=os.path.join(self.mesh_dir, "./assets/plates/stamling_rack.obj"),
+        #     scale=(1, 1, 1),
+        #     color=np.array([216, 215, 211, 255], dtype=np.float32) / 255,
+        #     name="rack",
+        #     body_type="dynamic",
+        #     initial_pose=sapien.Pose(p=[0, 0, 0]),
+        #     use_texture=False,
+        # )
+        # self.rack_extents = get_actor_obb(self.rack).extents
+
+        # --- Goal site ---
+        self.goal_site = actors.build_box(
+            self.scene,
+            half_sizes=[
+                0.1, 0.1, 0.1
+            ],
+            color=[0, 1, 0, 0.4],
+            name="goal_site",
+            body_type="kinematic",
+            add_collision=False,
+            initial_pose=sapien.Pose(
+                p = [0.3,-0.63,0.6]
+            ),
+        )
+        self.goal_extents = np.array(
+            [0.2, 0.2, 0.2]
+        )
+
+    def _settle_actor(self, actor, max_steps=200, threshold=1e-4):
+        # waits until actor stops moving before proceeding
+        for _ in range(max_steps):
+            prev_pos = actor.pose.p.clone()
+            self.scene.step()
+            curr_pos = actor.pose.p
+            
+            if torch.allclose(prev_pos, curr_pos, atol=threshold):
+                break  # Object has settled
+
+    @property
+    def _default_human_render_camera_configs(self):
+        # import pdb; pdb.set_trace()
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            # pose = look_at([1.4, -1, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+            pose = look_at([2.1, 0, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        # import pdb; pdb.set_trace()
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+    
+    def _recreate_ycb_obstacle(self, name, visual_path, collision_path, scale):
+        # creating one ycb object as obstacle with textur
+        ## REMOVE EXISTING OBJECT ##
+
+        # if name in self.table_scene.ycb_objects:
+        #     wrapped = self.table_scene.ycb_objects[name]['actor']
+        #     # 1. Remove all physics entities manually
+        #     for ent in wrapped._objs:
+        #         self.table_scene.scene.sub_scenes[0].remove_entity(ent)
+
+        #     # 2. Remove from scene registry
+        #     if name in self.table_scene.scene.actors:
+        #         del self.table_scene.scene.actors[name]
+
+        #     # 3. Remove from state_dict registry
+        #     reg = self.table_scene.scene.state_dict_registry.actors
+        #     if name in reg:
+        #         del reg[name]
+
+        #     # 4. Remove from YCB registry
+        #     del self.table_scene.ycb_objects[name]
+
+        # CREATE NEW OBJECT ##
+        builder = self.table_scene.scene.create_actor_builder()
+
+        # Object params
+        # p,q,x,y,z = self.computeJitter()
+        # scale = [x,y,z]
+
+        # add tecture
+        builder.add_visual_from_file(
+                filename=os.path.join(self.table_scene.object_path_prefix, visual_path),
+                scale=scale,
+            )
+        
+        # add collision
+        # Check if the path is a directory containing multiple obj files
+        if os.path.isdir(collision_path):
+            collision_paths = [os.path.join(collision_path, f) for f in os.listdir(collision_path) 
+                                if f.endswith('.obj')]
+        else:
+            collision_paths = [collision_path]
+
+        for collision_path in collision_paths:
+                builder.add_multiple_convex_collisions_from_file(
+                    filename=collision_path,
+                    scale=scale,
+                    material=None,
+                    density=1000,
+                )
+
+        obj = builder.build(name)
+
+        # # get obj height
+        # extents = get_actor_obb(obj).extents
+        # # print("obj extents: ", extents)
+        # p[2] = 0.045 + extents[2] / 2  # Adjust z to place on table surface
+
+        # # Set pose from config
+        obj.set_pose(sapien.Pose(p=[0.25, 0, 0.2], q=[1, 0, 0, 0]))
+        
+
+        setattr(self, f"ycb_object_{name}", obj)
+        self.obstacle_actors[name] = obj
+        # self.table_scene.ycb_objects[name] = {
+        #         'actor': obj,
+        #         'points': None,
+        #         'position': p,
+        #         'quaternion':q
+        #     }
+        
+    
+    def _recreate_ycb_objects(self, config_file):
+        """For recreating ycb objects from a specific yaml file. Used to randomize scale every reset"""
+
+        all_ycb_points = []
+
+        object_config_path = os.path.join(ENV_CONFIG_DIR, config_file)
+
+        with open(object_config_path, 'r') as f:
+            config = yaml.safe_load(f)
+
+        for obj_config in config['objects']:
+            name = obj_config['name']
+
+            ## REMOVE EXISTING OBJECT ##
+            if name in self.table_scene.ycb_objects:
+                wrapped = self.table_scene.ycb_objects[name]['actor']
+                # 1. Remove all physics entities manually
+                for ent in wrapped._objs:
+                    self.table_scene.scene.sub_scenes[0].remove_entity(ent)
+
+                # 2. Remove from scene registry
+                if name in self.table_scene.scene.actors:
+                    del self.table_scene.scene.actors[name]
+
+                # 3. Remove from state_dict registry
+                reg = self.table_scene.scene.state_dict_registry.actors
+                if name in reg:
+                    del reg[name]
+
+                # 4. Remove from YCB registry
+                del self.table_scene.ycb_objects[name]
+
+            ## CREATE NEW OBJECT ##
+            builder = self.table_scene.scene.create_actor_builder()
+            
+            # Object params
+            p,q,x,y,z = self.computeJitter()
+            scale = [x,y,z]
+            
+            # Add visual mesh if specified
+            if 'visual' in obj_config:
+                builder.add_visual_from_file(
+                    filename=os.path.join(self.table_scene.object_path_prefix, obj_config['visual']['path']),
+                    scale=scale,
+                )
+            
+            # Add collision mesh (required)
+            collision_config = obj_config['collision']
+            collision_path = os.path.join(self.table_scene.object_path_prefix, collision_config['path'])
+            
+            # Check if the path is a directory containing multiple obj files
+            if os.path.isdir(collision_path):
+                collision_paths = [os.path.join(collision_path, f) for f in os.listdir(collision_path) 
+                                  if f.endswith('.obj')]
+            else:
+                collision_paths = [collision_path]
+            
+            # Create point cloud using trimesh if required
+            if self.table_scene.create_obstacle_point_cloud:
+                for collision_path in collision_paths:
+                    points = self.table_scene._create_point_cloud_from_mesh(collision_path, scale, obj_config)
+                    # print("object name: ", obj_config['name'])
+                    # print("object points size: ", points.shape)
+                    all_ycb_points.append(points)  # Append transformed points
+            
+            ###################
+            for collision_path in collision_paths:
+                builder.add_multiple_convex_collisions_from_file(
+                    filename=collision_path,
+                    scale=scale,
+                    material=None,
+                    density=collision_config.get('density', 1000),
+                )
+            ###################
+            # Build actor as static or dynamic based on config
+            if obj_config.get('static', True):
+                obj = builder.build_static(obj_config['name'])
+                # print(obj_config['name'], " is static")
+            else:
+                obj = builder.build(obj_config['name'])
+                # print(obj_config['name'], " is dynamic")
+            
+            # get obj height
+            extents = get_actor_obb(obj).extents
+            # print("obj extents: ", extents)
+            p[2] =  extents[2] / 2  # Adjust z to place on table surface
+
+            # Set pose from config
+            obj.set_pose(sapien.Pose(
+                p = p,
+                q = q
+            ))
+            
+            if obj.name == "table": # TODO, send this by variable
+                self.table_scene.primary_objects_ids.append((obj.per_scene_id).item()) #or obj._bodies[0].entity.global_id; 
+            elif obj.name == "box":
+                self.box = obj
+
+            # Store reference to object and its point cloud
+            setattr(self, f"ycb_object_{obj_config['name']}", obj)
+            self.table_scene.ycb_objects[obj_config['name']] = {
+                'actor': obj,
+                'points': points,
+                'position': np.array(obj_config['pose']['position']),
+                'quaternion': np.array(obj_config['pose']['quaternion'])
+            }
+        
+        # Return the point cloud instead of setting it directly
+        if all_ycb_points:
+            return np.vstack(all_ycb_points)
+        return None
+    
+    def computeJitter(self, b=1):
+        #max jitter range
+        x_range = 0.15
+        y_range = 0.3
+        roll = np.pi
+        x_sc_range = 0.1
+        y_sc_range = 0.015
+        z_sc_range = 0.175
+
+        # base xyz
+        x = torch.ones((b, 1)) * 0.65
+        y = torch.ones((b, 1)) * 0
+        z = torch.ones((b, 1)) * 0.07
+
+        # base rotation (ZYX → roll = π)
+        euler = torch.zeros((b, 3), dtype=torch.float32)
+        euler[:, 0] = np.pi
+
+        # init jitter
+        x_rand = torch.zeros((b, 1))
+        y_rand = torch.zeros((b, 1))
+        roll_jit = torch.zeros((b, 1))
+        x_sc, y_sc, z_sc = 0.2, 0.165, 0.325
+
+        # XY jitter
+        if self.rand_level == 1:
+            x_rand = torch.empty(b, 1).uniform_(-x_range/2, x_range/2)
+            y_rand = torch.empty(b, 1).uniform_(-y_range/2, y_range/2)
+            roll_jit = torch.empty(b, 1).uniform_(-roll/8, roll/8)
+            #scale jitter
+            x_sc = x_sc  + random.uniform(-x_sc_range/2,  x_sc_range/2)   # -> 0.1 to 0.3
+            y_sc = y_sc + random.uniform(-y_sc_range/2, y_sc_range/2)  # -> 0.15 to 0.18
+            # z_sc = z_sc + random.uniform(-z_sc_range/2,z_sc_range/2) # -> 0.15 to 0.5
+
+        elif self.rand_level >= 2:
+            x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+            y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+            roll_jit = torch.empty(b, 1).uniform_(-roll / 4, roll / 4)
+            #scale jitter
+            x_sc = x_sc  + random.uniform(-x_sc_range,  x_sc_range)   # -> 0.1 to 0.3
+            y_sc = y_sc + random.uniform(-y_sc_range, y_sc_range)  # -> 0.1 to 0.18
+            # z_sc = z_sc + random.uniform(-z_sc_range,z_sc_range) # -> 0.15 to 0.5
+
+        x = x + x_rand
+        y = y + y_rand
+        euler[:, 0] = euler[:, 0] + roll_jit
+
+        # compose final pose
+        p = torch.cat((x, y, z), dim=1)
+        q = rotation_conversions.euler_to_quaternion(euler)
+        p_np = p[0].detach().cpu().numpy().astype(np.float32)
+        q_np = q[0].detach().cpu().numpy().astype(np.float32) 
+
+        return p_np, q_np, x_sc, y_sc, z_sc
+
+
+    def _load_new_ycb_obstacles(self):
+        """
+        Load obstacles into the scene using nonconvex mesh building.
+        """
+        ycb_asset_table = self.model_db['ycb']
+        model_keys = list(ycb_asset_table.keys())
+
+        # print(type(self.scene))
+        # print(dir(self.scene))
+
+
+        # Randomly select obstacle keys
+        self.obstacle_names = [
+            str(s)
+            for s in self._episode_rng.choice(
+                model_keys,
+                self._distractor_count,
+                replace=False
+            )
+        ]
+        # self.obstacle_names=["my_box"]  # For testing, use only one box
+
+        for name in self.obstacle_names:
+            scale = ycb_asset_table[name].get('scale', 1.0)
+            visual_mesh_path = ycb_asset_table[name].get('visual_mesh_path')
+            collision_mesh_path = ycb_asset_table[name].get('collision_mesh_path')
+            # breakpoint()
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            obj_collision_path = self._get_asset_path(collision_mesh_path)
+
+            self._recreate_ycb_obstacle(name, obj_visual_path, obj_collision_path, scale=(scale, scale, scale) if isinstance(scale, (int, float)) else tuple(scale))
+
+            # # Use ManiSkill's higher-level helper with texture support
+            # actor = actors.build_nonconvex_from_mesh(
+            #     scene=self.scene,
+            #     mesh_path=obj_visual_path,
+            #     scale=(scale, scale, scale) if isinstance(scale, (int, float)) else tuple(scale),
+            #     color=np.array([0.7, 0.7, 0.7, 1], dtype=np.float32),  # fallback color
+            #     name=name,
+            #     body_type="dynamic",
+            #     add_collision=True,
+            #     initial_pose=sapien.Pose(p=[0.25, 0, 0.2], q=[1, 0, 0, 0]),
+            #     use_texture=True,  # Enable texture loading
+            # )
+            # self.table_scene._create_point_cloud_from_mesh(mesh_path=obj_collision_path, scale=scale,obj_config=ycb_asset_table[name]
+            # Store reference
+            # self.obstacle_actors[name] = actor
+
+
+
+
+
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            # self._set_env_ycb_obj_attr(name, obj)
+
+            # self._generated_object_configs[OBJECTS_KEY][name].update(
+            #     {
+            #         'visual': {
+            #             'path': visual_mesh_path,
+            #         },
+            #         'collision': {
+            #             'path': collision_mesh_path,
+            #         },
+            #         'scale': scale,
+            #     }
+            # )
+
+    
+    def _get_asset_path(self, asset):
+        resolved_asset = format_path(asset)
+        return (
+            str(pathlib.Path(self.asset_root) / resolved_asset) 
+            if not os.path.isabs(asset) 
+            else resolved_asset
+        )
+
+    def _load_actor_helper(self,
+        scene: sapien.Scene,
+        scale: float | tuple[float, float, float] | None = None,
+        physical_material: PhysxMaterial = None,
+        density: float | None = None,
+        visual_mesh_path: str | None = None,
+        collision_mesh_path: str | None = None,
+        ):
+
+        """Load an actor from a model ID."""
+        builder = scene.create_actor_builder()
+
+        if isinstance(scale, (int, float)):
+            scale = (scale, scale, scale)
+        
+        if visual_mesh_path is not None:
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            builder.add_visual_from_file(
+                filename=obj_visual_path, 
+                scale=scale
+            )
+        
+        obj_collision_path = self._get_asset_path(collision_mesh_path)
+        builder.add_multiple_convex_collisions_from_file(
+            filename=obj_collision_path,
+            scale=scale,
+            material=physical_material,
+            density=density or 1000,
+        )
+
+        return builder
+
+    def _initialize_new_scene_obstacles(self):
+        """Set the random positions of the obstacles."""
+        placement_bounds = PlacementBounds(*self._object_place_bounds)
+
+        # Build bounding boxes for any fixed objects (bowl + rack)
+        placed_obstacles = []
+        for fixed_actor in [self.box]:
+            obb = get_actor_obb(fixed_actor)     # trimesh Box
+            bb_min, bb_max = obb.bounds          # (2,3) array
+            placed_obstacles.append(
+                (fixed_actor, ObjectBoundingBox(bb_min, bb_max))
+            )
+
+        # Place new obstacles
+        for obs_actor_name in self.obstacle_names:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            quat = [1, 0, 0, 0]  # upright
+
+            obb = get_actor_obb(obs_actor)
+            bb_min, bb_max = obb.bounds
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+
+            success = False
+            while not success:
+                valid_position_2d = find_valid_position_2d(
+                    placement_bounds=placement_bounds,
+                    actor=obs_actor,
+                    actor_local_bbox=bbox,          # pass ObjectBoundingBox
+                    placed_objects=placed_obstacles,
+                    rng=self._episode_rng,
+                    retries=5000,
+                    minimum_aabb_separation=0.01,
+                )
+                # Place object on table surface
+                z = -bb_min[2]
+                valid_position = np.hstack([valid_position_2d, z/2])
+                # print(f"Placing {obs_actor_name} at {valid_position}")
+                obs_actor.set_pose(sapien.Pose(valid_position, quat))
+
+                # Update point cloud if needed
+                if self.create_obstacle_point_cloud:
+                    config = {"pose": {"position": None, "quaternion": None}}
+                    config['pose']['position'] = valid_position
+                    config['pose']['quaternion'] = quat
+                    self._update_pc_with_obstacle(obs_actor_name, config)
+
+                # # Let physics settle
+                # self._settle_distractor(
+                #     distractor_obj=obs_actor,
+                #     motion_lock_axes=(1, 1, 0, 1, 1, 1),
+                # )
+                # breakpoint()
+                pos = obs_actor.pose.p
+                if isinstance(pos, torch.Tensor):
+                    pos = pos.detach().cpu().numpy().reshape(-1)
+                else:
+                    pos = np.array(pos, dtype=float).reshape(-1)
+
+                success = True
+                # if 0 <= pos[2] < 0.4:
+                #     success = True
+
+            # Update placed obstacles
+            obb = get_actor_obb(obs_actor)
+            bb_min, bb_max = obb.bounds
+            placed_obstacles.append(
+                (obs_actor, ObjectBoundingBox(bb_min, bb_max))
+            )
+
+        # # Unlock motion + stabilize
+        # for obs_actor_name in self.obstacle_names:
+        #     obs_actor = self.obstacle_actors[obs_actor_name]
+
+        #     # Check if actor is static by its dynamics type
+        #     if hasattr(obs_actor, "dynamics_type") and obs_actor.dynamics_type == "static":
+        #         continue
+
+        #     obs_actor.lock_motion(0, 0, 0, 0, 0, 0)
+        #     obs_actor.set_velocity(np.zeros(3))
+        #     obs_actor.set_angular_velocity(np.zeros(3))
+        #     self._settle(0.5)
+
+
+        # print(f"Finished placing {len(self.obstacle_names)} obstacles.")
+
+    def _update_pc_with_obstacle(self, name:str, config):
+        ycb_asset_table = self.model_db['ycb']
+        scale = ycb_asset_table[name].get('scale', 1.0)
+        collision_mesh_path = ycb_asset_table[name].get('collision_mesh_path')
+        obj_collision_path = self._get_asset_path(collision_mesh_path)
+        points = self.table_scene._create_point_cloud_from_mesh(mesh_path=obj_collision_path, scale=scale,obj_config=config)
+        self.table_scene.obstacle_point_cloud = np.vstack([self.base_pc, points]) if self.base_pc is not None else points
+
+    def compute_dense_reward(self, obs: Any, action: Array, info: Dict):
+        return 0
+
+def local_bbox_for_actor(actor: sapien.Entity) -> tuple[np.ndarray, np.ndarray]:
+    mins, maxs = get_axis_aligned_bbox_for_actor(actor)
+    mins -= actor.pose.p
+    maxs -= actor.pose.p
+    return mins, maxs
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54fb45f967906311d3779d9a390b9718b98d8b6
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles.py
@@ -0,0 +1,510 @@
+# additional checks for actor building from meshes
+from typing import Any, Dict, Union
+
+import os
+import yaml
+import numpy as np
+import sapien
+from sapien.physx import PhysxMaterial
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+import pathlib
+
+from mani_skill.envs.tasks.tabletop.grasp_bowl_v0 import GraspBowlEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.building import actors
+from mani_skill.utils.geometry import rotation_conversions, get_axis_aligned_bbox_for_actor
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from mani_skill.examples.motionplanning.noahbiarm.utils import get_3d_bbox, get_actor_obb
+from mani_skill.agents.robots import Panda
+from mani_skill import format_path
+from cfdp.utils.object_placement_2 import PlacementBounds, ObjectBoundingBox, find_valid_position_2d
+
+from msx_envs.agent import PandaSphere
+from msx_envs import assets
+
+from .scene_loader_builder import SceneLoaderBuilder
+
+
+@register_env("GraspCupObstacles-v0", max_episode_steps=500)
+class GraspCupObstaclesEnv(GraspBowlEnv):
+    def __init__(
+        self, 
+        object_config_dir="base_configs/base_table_shelf.yaml", 
+        create_obstacle_point_cloud=True, 
+        is_inference=False, 
+        model_config="asset_tables.yaml",
+        asset_root=str(pathlib.Path(assets.__file__).parent),
+        distractor_count: int = 5,
+        **kwargs
+        ):
+
+        SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+        self.agent: Union[Panda, PandaSphere]
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        self.model_configs = model_config
+        self.asset_root = asset_root
+        self._distractor_count = distractor_count
+        self._object_place_bounds = None # Calculated later
+        self.obstacle_actors = {} # Obstacle actors in the scene
+
+        with open(self._get_asset_path(self.model_configs), 'r') as f:
+            print("Loading model config from: ", self._get_asset_path(self.model_configs))
+            self.model_db = yaml.safe_load(f)
+
+        super().__init__(**kwargs)
+
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params()
+        # Add any additional initialization for GraspBowlV1Env here
+    
+    def _get_obs_extra(self, info: Dict):
+        # check for grasping
+        self.grasped = max(self.grasped , self.agent.is_grasping(self.bowl))
+        # print("!!current grasped self.agent.is_grasping(self.bowl) is: ", self.agent.is_grasping(self.bowl))
+
+        if not self.grasped:
+            goal_pose = self.compute_grasp_pose()
+        else:
+            goal_pose = self.compute_release_pose()
+
+        if not self.is_inference:
+            # return extra observation, for training and data collection
+            obs = dict( #data collection setting
+                tcp_pose=self.agent.tcp.pose.raw_pose.detach().cpu().numpy(),
+                goal_pose=goal_pose.raw_pose.detach().cpu().numpy(),
+                goal_pos=goal_pose.p.detach().cpu().numpy(),
+                grasped=self.grasped,
+            )
+        else:
+            obs = dict( # inference setting
+                tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pose = goal_pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pos  = goal_pose.p.detach().cpu().numpy().squeeze(0),
+                grasped=self.grasped,
+            )
+        if self.create_obstacle_point_cloud:
+            # print("obstacle_point_cloud is: ", self.obstacle_point_cloud.shape)
+            obs.update({
+                'obstacle_point_cloud': self.table_scene.obstacle_point_cloud
+            })
+        return obs
+    
+    def _load_scene(self, options: dict):
+        print("mesh dir: ", self.mesh_dir)
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        max_iter = 10
+        for i in range(max_iter):
+            self._init_env_objects()  # init cup, rack, and goal site
+            self._load_new_ycb_obstacles() # load obstacles
+            try:
+                self.table_scene.build()
+                # self._initialize_new_scene_obstacles() # set initial state of obstacles
+                print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+    
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+    
+    def evaluate(self):
+        BOWL_D = self.bowl_extents[0]
+        goal_extents = torch.from_numpy(self.goal_extents.copy())
+        goal_p, _ = self.get_goal_site_pose()
+        is_bowl_placed = (
+            self.is_inside(self.bowl.pose.p, goal_p , goal_extents)
+        )
+
+        euler = rotation_conversions.quaternion_to_euler(self.bowl.pose.q)
+        is_bowl_vertical = (
+            ((torch.abs(torch.abs(euler[:, 1])) - np.pi/2) <= np.pi/4 )&
+            ((torch.abs(torch.abs(euler[:, 2])) - np.pi/2) <= np.pi/4 )
+        ) 
+
+        # tcp pose
+        tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy()
+        if self.tcp_pose_valid and ( (tcp_pose[:, 0] < -0.2).all() or (tcp_pose[:, 2] > 0.65).all()):
+            print("tcp pose is out of boundary!, x axis is: ", tcp_pose[:, 0])
+            self.tcp_pose_valid = False
+
+        not_grasping = torch.logical_not(self.agent.is_grasping(self.bowl))
+
+        # plate is static
+        is_bowl_static = self.bowl.is_static()
+        
+        tcp_pose_valid_tensor = torch.tensor(self.tcp_pose_valid, device=is_bowl_placed.device)
+        success = torch.logical_and(is_bowl_placed, tcp_pose_valid_tensor)
+
+        return {
+            "success": success
+        }
+
+    
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        print("-----Initialize episode panda------")
+        # self.rand_level = 2
+        print(f"rand level is: {self.rand_level}")
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+
+            if self.rand_level >= 5:
+                side = torch.randint(0, 2, (b, 1)) * 2 - 1
+            else:
+                side = torch.ones((b, 1))
+
+            x_range = 0.15
+            y_range = 0.15
+            ##################################################################################
+            # cup pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.3
+            y = -1 * side * torch.ones((b, 1)) * 0.7
+            z = torch.ones((b, 1)) * 0.01
+
+            # default rotation
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 1] = np.pi # set cup upright
+            
+            # ---- Apply randomness by level ----
+            # default no jitter
+            device = self.device if hasattr(self, "device") else torch.device("cpu")
+            dtype = torch.float32
+            x_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+            y_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+            
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                roll_jit = torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+
+           # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b,3)
+            # xyz = torch.tensor([[0.3, -0.7, 0.0]])
+            p = robot_p + xyz                   # (b,3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b,4)  (check expected euler order)
+            self.bowl.set_pose(Pose.create_from_pq(p=p, q=q))
+
+            ##################################################################################
+            # rack pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.25
+            y = 1 * side * torch.ones((b, 1)) * 0.55
+            z = torch.zeros((b, 1))
+
+            # default rotation (keep ZYX comment consistent with your conversion)
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 0] = np.pi  # roll
+
+            # default no jitter
+            x_rand = torch.zeros((b, 1), dtype=torch.float32)
+            y_rand = torch.zeros((b, 1), dtype=torch.float32)
+
+            # ---- Apply randomness by level ----
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                # rotation jitter around roll (optionally add yaw jitter if needed)
+                roll_jit =  torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+                # Optional:
+                # euler[:, 2] = euler[:, 2] + torch.empty((b,), dtype=dtype, device=device).uniform_(-np.pi/12, np.pi/12)
+
+            # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b, 3)
+            p = robot_p + xyz                   # (b, 3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b, 4)
+                        
+            self.rack.set_pose(Pose.create_from_pq(p=p, q=q))
+            # gp, gq = self.get_goal_site_pose()
+            # self.goal_site.set_pose(Pose.create_from_pq(p=gp, q=gq))
+
+    def _init_env_objects(self):
+        """Initialize cup, rack, and goal site actors in the scene."""
+        # --- Cup (**Named as bowl**) ---
+        self.bowl = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/kalas_mug.obj"),
+            scale=(1, 1, 1),
+            color=np.array([0, 0, 255, 255], dtype=np.float32) / 255,
+            name="bowl",
+            body_type="dynamic",
+            add_collision=True,
+            initial_pose=sapien.Pose(p=[0, 0, 0], q=[1, 0, 0, 0]),
+        )
+        self.bowl_extents = get_actor_obb(self.bowl).extents
+
+        # --- Rack ---
+        self.rack = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/stamling_rack.obj"),
+            scale=(1, 1, 1),
+            color=np.array([216, 215, 211, 255], dtype=np.float32) / 255,
+            name="rack",
+            body_type="dynamic",
+            initial_pose=sapien.Pose(p=[0, 0, 0]),
+        )
+        self.rack_extents = get_actor_obb(self.rack).extents
+
+        # --- Goal site ---
+        self.goal_site = actors.build_box(
+            self.scene,
+            half_sizes=[
+                self.rack_extents[0] / 2 / 4,
+                self.rack_extents[1] / 2 * 0.9,
+                self.rack_extents[2] / 2,
+            ],
+            color=[0, 1, 0, 0],
+            name="goal_site",
+            body_type="kinematic",
+            add_collision=False,
+            initial_pose=sapien.Pose(
+                p=[+self.rack_extents[0] * 9.5 / 14 / 2, 0, self.rack_extents[2] / 2]
+            ),
+        )
+        self.goal_extents = np.array(
+            [self.rack_extents[0] / 4, self.rack_extents[1] * 0.9, self.rack_extents[2]]
+        )
+    
+    @property
+    def _default_human_render_camera_configs(self):
+        # import pdb; pdb.set_trace()
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            # pose = look_at([1.4, -1, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+            pose = look_at([2, 0, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        # import pdb; pdb.set_trace()
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+
+    def _load_new_ycb_obstacles(self):
+        """
+        Load obstacles into the scene
+        """
+        ycb_asset_table = self.model_db['ycb']
+        model_keys = list(ycb_asset_table.keys())
+        self.obstacle_names = [ # randomly choose _distractor_count distractor keys
+            str(s) 
+            for s 
+            in self._episode_rng.choice(
+                model_keys, 
+                self._distractor_count, 
+                replace=False
+            )
+        ]
+
+        for name in self.obstacle_names:
+            scale=ycb_asset_table[name].get('scale', 1.0)
+            visual_mesh_path=ycb_asset_table[name].get('visual_mesh_path')
+            collision_mesh_path=ycb_asset_table[name].get('collision_mesh_path')
+            self.obstacle_actors[name] = self._load_actor_helper(
+                scene=self.scene,
+                scale=scale,
+                visual_mesh_path=visual_mesh_path,
+                collision_mesh_path=collision_mesh_path,
+            )
+            if hasattr(self.obstacle_actors[name], "find_component_by_type") == False:
+                print(f"!!!!!!!!!!!!!!!!!!!!!!![WARN] Actor {name} has no components!")
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            # self._set_env_ycb_obj_attr(name, obj)
+
+            # self._generated_object_configs[OBJECTS_KEY][name].update(
+            #     {
+            #         'visual': {
+            #             'path': visual_mesh_path,
+            #         },
+            #         'collision': {
+            #             'path': collision_mesh_path,
+            #         },
+            #         'scale': scale,
+            #     }
+            # )
+    
+    def _get_asset_path(self, asset):
+        resolved_asset = format_path(asset)
+        return (
+            str(pathlib.Path(self.asset_root) / resolved_asset) 
+            if not os.path.isabs(asset) 
+            else resolved_asset
+        )
+
+    def _load_actor_helper(
+        self,
+        scene: sapien.Scene,
+        scale: float | tuple[float, float, float] | None = None,
+        physical_material: PhysxMaterial = None,
+        density: float | None = None,
+        visual_mesh_path: str | None = None,
+        collision_mesh_path: str | None = None,
+        name: str = "obstacle",
+    ):
+        if isinstance(scale, (int, float)):
+            scale = (scale, scale, scale)
+
+        builder = scene.create_actor_builder()
+
+        # Add visual if available
+        if visual_mesh_path is not None:
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            builder.add_visual_from_file(filename=obj_visual_path, scale=scale)
+
+        # Try convex collision first
+        if collision_mesh_path is not None:
+            obj_collision_path = self._get_asset_path(collision_mesh_path)
+            builder.add_multiple_convex_collisions_from_file(
+                filename=obj_collision_path,
+                scale=scale,
+                material=physical_material,
+                density=density or 1000,
+            )
+
+        actor = builder.build(name)
+
+        # Fallback: if no collision/visual shapes, retry with nonconvex
+        if not actor.has_collision_shapes and not actor.has_visual_bodies:
+            print(f"[WARN] Convex failed for {collision_mesh_path}, retrying with nonconvex.")
+            builder = scene.create_actor_builder()
+            if visual_mesh_path is not None:
+                obj_visual_path = self._get_asset_path(visual_mesh_path)
+                builder.add_visual_from_file(filename=obj_visual_path, scale=scale)
+            builder.add_nonconvex_collision_from_file(
+                filename=obj_collision_path,
+                scale=scale,
+                material=physical_material,
+                density=density or 1000,
+            )
+            actor = builder.build(name)
+
+        return actor
+
+
+    def _initialize_new_scene_obstacles(self):
+        """Set the initial state of the newly generated obstacles only."""
+        if self._object_place_bounds is None:
+            self._object_place_bounds = [
+                [0.195, -0.2461],  # minimum x and y
+                [0.795, 1.1539],   # maximum x and y
+                [2.0]              # always [2]
+            ]
+
+        placement_bounds = PlacementBounds(*self._object_place_bounds)
+
+        # Build bounding boxes for fixed objects (bowl + rack)
+        placed_obstacles = []
+        for fixed_actor in [self.bowl, self.rack]:
+            bb_min, bb_max = local_bbox_for_actor(actor=fixed_actor)
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+            pos_np = np.array(fixed_actor.pose.p, dtype=float)  # convert tensor -> numpy
+            placed_obstacles.append((fixed_actor, bbox, pos_np))
+
+        # Place new obstacles
+        for obs_actor_name in self.obstacle_names:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            quat = [1, 0, 0, 0]  # upright
+            bb_min, bb_max = local_bbox_for_actor(actor=obs_actor)
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+
+            success = False
+            while not success:
+                valid_position_2d = find_valid_position_2d(
+                    placement_bounds=placement_bounds,
+                    actor=obs_actor,
+                    actor_local_bbox=bbox,
+                    placed_objects=placed_obstacles,  # now contains numpy poses
+                    rng=self._episode_rng,
+                    retries=5000,
+                    minimum_aabb_separation=0.01,
+                )
+                # Place object on table surface
+                z = -bb_min[2]
+                valid_position = np.hstack([valid_position_2d, z])
+                obs_actor.set_pose(sapien.Pose(valid_position, quat))
+
+                self._settle_distractor(
+                    distractor_obj=obs_actor,
+                    motion_lock_axes=(1, 1, 0, 1, 1, 1),
+                )
+
+                if 0 < obs_actor.pose.p[2] < 0.4:
+                    success = True
+
+            # Update placed obstacles list
+            bb_min, bb_max = local_bbox_for_actor(actor=obs_actor)
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+            pos_np = np.array(obs_actor.pose.p, dtype=float)
+            placed_obstacles.append((obs_actor, bbox, pos_np))
+
+        # Unlock motion + stabilize
+        for obs_actor_name in self.obstacle_names:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            if not isinstance(obs_actor, sapien.ActorStatic):
+                obs_actor.lock_motion(0, 0, 0, 0, 0, 0)
+                obs_actor.set_velocity(np.zeros(3))
+                obs_actor.set_angular_velocity(np.zeros(3))
+                self._settle(0.5)
+
+        print(f"Finished placing {len(self.obstacle_names)} obstacles.")
+
+
+def local_bbox_for_actor(actor: sapien.Entity) -> tuple[np.ndarray, np.ndarray]:
+    mins, maxs = get_axis_aligned_bbox_for_actor(actor)
+    mins -= actor.pose.p
+    maxs -= actor.pose.p
+    return mins, maxs
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles_v1.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a84b0a6bfb32797c2c9b965db6f83e0bd75de5f
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_obstacles_v1.py
@@ -0,0 +1,550 @@
+# Issue with actor_builder
+from typing import Any, Dict, Union
+
+import os
+import yaml
+import numpy as np
+import sapien
+from sapien.physx import PhysxMaterial
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+import pathlib
+
+from mani_skill.envs.tasks.tabletop.grasp_bowl_v0 import GraspBowlEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.building import actors
+from mani_skill.utils.geometry import rotation_conversions, get_axis_aligned_bbox_for_actor
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from mani_skill.examples.motionplanning.noahbiarm.utils import get_3d_bbox, get_actor_obb
+from mani_skill.agents.robots import Panda
+from mani_skill import format_path
+from cfdp.utils.object_placement_2 import PlacementBounds, ObjectBoundingBox, find_valid_position_2d
+
+from msx_envs.agent import PandaSphere
+from msx_envs import assets
+
+from .scene_loader_builder import SceneLoaderBuilder
+
+
+@register_env("GraspCupObstacles-v1", max_episode_steps=500)
+class GraspCupObstaclesEnv(GraspBowlEnv):
+    def __init__(
+        self, 
+        object_config_dir="base_configs/base_table_shelf.yaml", 
+        create_obstacle_point_cloud=True, 
+        is_inference=False, 
+        model_config="asset_tables.yaml",
+        asset_root=str(pathlib.Path(assets.__file__).parent),
+        distractor_count: int = 4,
+        **kwargs
+        ):
+
+        SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+        self.agent: Union[Panda, PandaSphere]
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        self.model_configs = model_config
+        self.asset_root = asset_root
+        self._distractor_count = distractor_count
+        self._object_place_bounds = None # Calculated later
+        self.obstacle_actors = {} # Obstacle actors in the scene
+        self.base_pc = None
+
+        with open(self._get_asset_path(self.model_configs), 'r') as f:
+            print("Loading model config from: ", self._get_asset_path(self.model_configs))
+            self.model_db = yaml.safe_load(f)
+
+        super().__init__(**kwargs)
+
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params()
+        # Add any additional initialization for GraspBowlV1Env here
+    
+    def _get_obs_extra(self, info: Dict):
+        # check for grasping
+        self.grasped = max(self.grasped , self.agent.is_grasping(self.bowl))
+        # print("!!current grasped self.agent.is_grasping(self.bowl) is: ", self.agent.is_grasping(self.bowl))
+
+        if not self.grasped:
+            goal_pose = self.compute_grasp_pose()
+        else:
+            goal_pose = self.compute_release_pose()
+
+        if not self.is_inference:
+            # return extra observation, for training and data collection
+            obs = dict( #data collection setting
+                tcp_pose=self.agent.tcp.pose.raw_pose.detach().cpu().numpy(),
+                goal_pose=goal_pose.raw_pose.detach().cpu().numpy(),
+                goal_pos=goal_pose.p.detach().cpu().numpy(),
+                grasped=self.grasped,
+            )
+        else:
+            obs = dict( # inference setting
+                tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pose = goal_pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pos  = goal_pose.p.detach().cpu().numpy().squeeze(0),
+                grasped=self.grasped,
+            )
+        if self.create_obstacle_point_cloud:
+            # print("obstacle_point_cloud is: ", self.obstacle_point_cloud.shape)
+            obs.update({
+                'obstacle_point_cloud': self.table_scene.obstacle_point_cloud
+            })
+        return obs
+    
+    def _load_scene(self, options: dict):
+        print("mesh dir: ", self.mesh_dir)
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        self.obstacle_actors.clear()
+        self.obstacle_names = []
+
+        max_iter = 10
+        for i in range(max_iter):
+            # self.table_scene.obstacle_point_cloud = None
+            self._init_env_objects()  # init cup, rack, and goal site
+            self._load_new_ycb_obstacles() # load obstacles
+            try:
+                self.table_scene.build()
+                self.base_pc = self.table_scene.obstacle_point_cloud
+                print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+    
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+    
+    def evaluate(self):
+        BOWL_D = self.bowl_extents[0]
+        goal_extents = torch.from_numpy(self.goal_extents.copy())
+        goal_p, _ = self.get_goal_site_pose()
+        is_bowl_placed = (
+            self.is_inside(self.bowl.pose.p, goal_p , goal_extents)
+        )
+
+        euler = rotation_conversions.quaternion_to_euler(self.bowl.pose.q)
+        is_bowl_vertical = (
+            ((torch.abs(torch.abs(euler[:, 1])) - np.pi/2) <= np.pi/4 )&
+            ((torch.abs(torch.abs(euler[:, 2])) - np.pi/2) <= np.pi/4 )
+        ) 
+
+        # tcp pose
+        tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy()
+        if self.tcp_pose_valid and ( (tcp_pose[:, 0] < -0.2).all() or (tcp_pose[:, 2] > 0.65).all()):
+            print("tcp pose is out of boundary!, x axis is: ", tcp_pose[:, 0])
+            self.tcp_pose_valid = False
+
+        not_grasping = torch.logical_not(self.agent.is_grasping(self.bowl))
+
+        # plate is static
+        is_bowl_static = self.bowl.is_static()
+        
+        tcp_pose_valid_tensor = torch.tensor(self.tcp_pose_valid, device=is_bowl_placed.device)
+        success = torch.logical_and(is_bowl_placed, tcp_pose_valid_tensor)
+
+        return {
+            "success": success
+        }
+
+    
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        print("-----Initialize episode panda------")
+        # self.rand_level = 2
+        print(f"rand level is: {self.rand_level}")
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+
+            if self.rand_level >= 5:
+                side = torch.randint(0, 2, (b, 1)) * 2 - 1
+            else:
+                side = torch.ones((b, 1))
+
+            x_range = 0.15
+            y_range = 0.15
+            ##################################################################################
+            # cup pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.3
+            y = -1 * side * torch.ones((b, 1)) * 0.7
+            z = torch.ones((b, 1)) * 0.01
+
+            # default rotation
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 1] = np.pi # set cup upright
+            
+            # ---- Apply randomness by level ----
+            # default no jitter
+            device = self.device if hasattr(self, "device") else torch.device("cpu")
+            dtype = torch.float32
+            x_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+            y_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+            
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                roll_jit = torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+
+           # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b,3)
+            # xyz = torch.tensor([[0.3, -0.7, 0.0]])
+            p = robot_p + xyz                   # (b,3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b,4)  (check expected euler order)
+            self.bowl.set_pose(Pose.create_from_pq(p=p, q=q))
+
+            ##################################################################################
+            # rack pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.25
+            y = 1 * side * torch.ones((b, 1)) * 0.55
+            z = torch.zeros((b, 1))
+
+            # default rotation (keep ZYX comment consistent with your conversion)
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 0] = np.pi  # roll
+
+            # default no jitter
+            x_rand = torch.zeros((b, 1), dtype=torch.float32)
+            y_rand = torch.zeros((b, 1), dtype=torch.float32)
+
+            # ---- Apply randomness by level ----
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                # rotation jitter around roll (optionally add yaw jitter if needed)
+                roll_jit =  torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+                # Optional:
+                # euler[:, 2] = euler[:, 2] + torch.empty((b,), dtype=dtype, device=device).uniform_(-np.pi/12, np.pi/12)
+
+            # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b, 3)
+            p = robot_p + xyz                   # (b, 3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b, 4)
+                        
+            self.rack.set_pose(Pose.create_from_pq(p=p, q=q))
+            # gp, gq = self.get_goal_site_pose()
+            # self.goal_site.set_pose(Pose.create_from_pq(p=gp, q=gq))
+            self._initialize_new_scene_obstacles() # set initial state of obstacles
+
+    def _init_env_objects(self):
+        """Initialize cup, rack, and goal site actors in the scene."""
+        # --- Cup (**Named as bowl**) ---
+        self.bowl = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/kalas_mug.obj"),
+            scale=(1, 1, 1),
+            color=np.array([0, 0, 255, 255], dtype=np.float32) / 255,
+            name="mug",
+            body_type="dynamic",
+            add_collision=True,
+            initial_pose=sapien.Pose(p=[0, 0, 0], q=[1, 0, 0, 0]),
+        )
+        self.bowl_extents = get_actor_obb(self.bowl).extents
+
+        # --- Rack ---
+        self.rack = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/stamling_rack.obj"),
+            scale=(1, 1, 1),
+            color=np.array([216, 215, 211, 255], dtype=np.float32) / 255,
+            name="rack",
+            body_type="dynamic",
+            initial_pose=sapien.Pose(p=[0, 0, 0]),
+        )
+        self.rack_extents = get_actor_obb(self.rack).extents
+
+        # --- Goal site ---
+        self.goal_site = actors.build_box(
+            self.scene,
+            half_sizes=[
+                self.rack_extents[0] / 2 / 4,
+                self.rack_extents[1] / 2 * 0.9,
+                self.rack_extents[2] / 2,
+            ],
+            color=[0, 1, 0, 0],
+            name="goal_site",
+            body_type="kinematic",
+            add_collision=False,
+            initial_pose=sapien.Pose(
+                p=[+self.rack_extents[0] * 9.5 / 14 / 2, 0, self.rack_extents[2] / 2]
+            ),
+        )
+        self.goal_extents = np.array(
+            [self.rack_extents[0] / 4, self.rack_extents[1] * 0.9, self.rack_extents[2]]
+        )
+    
+    @property
+    def _default_human_render_camera_configs(self):
+        # import pdb; pdb.set_trace()
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            # pose = look_at([1.4, -1, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+            pose = look_at([2, 0, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        # import pdb; pdb.set_trace()
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+
+    def _load_new_ycb_obstacles(self):
+        """
+        Load obstacles into the scene using nonconvex mesh building.
+        """
+        ycb_asset_table = self.model_db['ycb']
+        model_keys = list(ycb_asset_table.keys())
+
+        print(type(self.scene))
+        print(dir(self.scene))
+
+
+        # Randomly select obstacle keys
+        self.obstacle_names = [
+            str(s)
+            for s in self._episode_rng.choice(
+                model_keys,
+                self._distractor_count,
+                replace=False
+            )
+        ]
+        self.obstacle_names=["foam_brick"]
+
+        for name in self.obstacle_names:
+            scale = ycb_asset_table[name].get('scale', 1.0)
+            visual_mesh_path = ycb_asset_table[name].get('visual_mesh_path')
+            collision_mesh_path = ycb_asset_table[name].get('collision_mesh_path')
+
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            obj_collision_path = self._get_asset_path(collision_mesh_path)
+
+            # Use ManiSkill's higher-level helper instead of ActorBuilder
+            actor = actors.build_nonconvex_from_mesh(
+                scene=self.scene,
+                mesh_path=obj_collision_path,   # nonconvex builder expects one mesh path
+                scale=(scale, scale, scale) if isinstance(scale, (int, float)) else tuple(scale),
+                color=np.array([0.7, 0.7, 0.7, 1.0], dtype=np.float32),  # default grey
+                name=name,
+                body_type="dynamic",  # start as kinematic to avoid initial collisions
+                add_collision=True,
+                initial_pose=sapien.Pose(p=[0.25, 0, 0.2], q=[1, 0, 0, 0]),
+            )
+            # self.table_scene._create_point_cloud_from_mesh(mesh_path=obj_collision_path, scale=scale,obj_config=ycb_asset_table[name])
+
+            # Store reference
+            self.obstacle_actors[name] = actor
+
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            # self._set_env_ycb_obj_attr(name, obj)
+
+            # self._generated_object_configs[OBJECTS_KEY][name].update(
+            #     {
+            #         'visual': {
+            #             'path': visual_mesh_path,
+            #         },
+            #         'collision': {
+            #             'path': collision_mesh_path,
+            #         },
+            #         'scale': scale,
+            #     }
+            # )
+
+    
+    def _get_asset_path(self, asset):
+        resolved_asset = format_path(asset)
+        return (
+            str(pathlib.Path(self.asset_root) / resolved_asset) 
+            if not os.path.isabs(asset) 
+            else resolved_asset
+        )
+
+    def _load_actor_helper(self,
+        scene: sapien.Scene,
+        scale: float | tuple[float, float, float] | None = None,
+        physical_material: PhysxMaterial = None,
+        density: float | None = None,
+        visual_mesh_path: str | None = None,
+        collision_mesh_path: str | None = None,
+        ):
+
+        """Load an actor from a model ID."""
+        builder = scene.create_actor_builder()
+
+        if isinstance(scale, (int, float)):
+            scale = (scale, scale, scale)
+        
+        if visual_mesh_path is not None:
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            builder.add_visual_from_file(
+                filename=obj_visual_path, 
+                scale=scale
+            )
+        
+        obj_collision_path = self._get_asset_path(collision_mesh_path)
+        builder.add_multiple_convex_collisions_from_file(
+            filename=obj_collision_path,
+            scale=scale,
+            material=physical_material,
+            density=density or 1000,
+        )
+
+        return builder
+
+    def _initialize_new_scene_obstacles(self):
+        """Set the random positions of the obstacles."""
+        if self._object_place_bounds is None:
+            # TODO dynamically calculate this, for now just hardcode
+            self._object_place_bounds = [
+                [0.1, -0.2461],  # minimum x and y
+                [0.8, 0.6],   # maximum x and y
+                [2]              # always [2]
+            ]
+        # breakpoint()
+        placement_bounds = PlacementBounds(*self._object_place_bounds)
+
+        # Build bounding boxes for any fixed objects (bowl + rack)
+        placed_obstacles = []
+        for fixed_actor in [self.bowl, self.rack]:
+            obb = get_actor_obb(fixed_actor)     # trimesh Box
+            bb_min, bb_max = obb.bounds          # (2,3) array
+            placed_obstacles.append(
+                (fixed_actor, ObjectBoundingBox(bb_min, bb_max))
+            )
+
+        # Place new obstacles
+        for obs_actor_name in self.obstacle_names:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            quat = [1, 0, 0, 0]  # upright
+
+            obb = get_actor_obb(obs_actor)
+            bb_min, bb_max = obb.bounds
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+
+            success = False
+            while not success:
+                valid_position_2d = find_valid_position_2d(
+                    placement_bounds=placement_bounds,
+                    actor=obs_actor,
+                    actor_local_bbox=bbox,          # pass ObjectBoundingBox
+                    placed_objects=placed_obstacles,
+                    rng=self._episode_rng,
+                    retries=5000,
+                    minimum_aabb_separation=0.01,
+                )
+                # Place object on table surface
+                z = -bb_min[2]
+                valid_position = np.hstack([valid_position_2d, -0.1])
+                print(f"Placing {obs_actor_name} at {valid_position}")
+                obs_actor.set_pose(sapien.Pose(valid_position, quat))
+
+                # Update point cloud if needed
+                if self.create_obstacle_point_cloud:
+                    config = {"pose": {"position": None, "quaternion": None}}
+                    config['pose']['position'] = valid_position
+                    config['pose']['quaternion'] = quat
+                    self._update_pc_with_obstacle(obs_actor_name, config)
+
+                # # Let physics settle
+                # self._settle_distractor(
+                #     distractor_obj=obs_actor,
+                #     motion_lock_axes=(1, 1, 0, 1, 1, 1),
+                # )
+                # breakpoint()
+                pos = obs_actor.pose.p
+                if isinstance(pos, torch.Tensor):
+                    pos = pos.detach().cpu().numpy().reshape(-1)
+                else:
+                    pos = np.array(pos, dtype=float).reshape(-1)
+
+                success = True
+                # if 0 <= pos[2] < 0.4:
+                #     success = True
+
+            # Update placed obstacles
+            obb = get_actor_obb(obs_actor)
+            bb_min, bb_max = obb.bounds
+            placed_obstacles.append(
+                (obs_actor, ObjectBoundingBox(bb_min, bb_max))
+            )
+
+        # # Unlock motion + stabilize
+        # for obs_actor_name in self.obstacle_names:
+        #     obs_actor = self.obstacle_actors[obs_actor_name]
+
+        #     # Check if actor is static by its dynamics type
+        #     if hasattr(obs_actor, "dynamics_type") and obs_actor.dynamics_type == "static":
+        #         continue
+
+        #     obs_actor.lock_motion(0, 0, 0, 0, 0, 0)
+        #     obs_actor.set_velocity(np.zeros(3))
+        #     obs_actor.set_angular_velocity(np.zeros(3))
+        #     self._settle(0.5)
+
+
+        print(f"Finished placing {len(self.obstacle_names)} obstacles.")
+
+    def _update_pc_with_obstacle(self, name:str, config):
+        ycb_asset_table = self.model_db['ycb']
+        scale = ycb_asset_table[name].get('scale', 1.0)
+        collision_mesh_path = ycb_asset_table[name].get('collision_mesh_path')
+        obj_collision_path = self._get_asset_path(collision_mesh_path)
+        points = self.table_scene._create_point_cloud_from_mesh(mesh_path=obj_collision_path, scale=scale,obj_config=config)
+        self.table_scene.obstacle_point_cloud = np.vstack([self.base_pc, points]) if self.base_pc is not None else points
+
+def local_bbox_for_actor(actor: sapien.Entity) -> tuple[np.ndarray, np.ndarray]:
+    mins, maxs = get_axis_aligned_bbox_for_actor(actor)
+    mins -= actor.pose.p
+    maxs -= actor.pose.p
+    return mins, maxs
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_v1.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_v1.py
new file mode 100644
index 0000000000000000000000000000000000000000..30f8001a06ad17527866ddc7fb4a040f0106106a
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/grasp_cup_v1.py
@@ -0,0 +1,307 @@
+from typing import Any, Dict, Union
+
+import os
+import numpy as np
+import sapien
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+
+from mani_skill.envs.tasks.tabletop.grasp_bowl_v0 import GraspBowlEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.building import actors
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.sapien_utils import look_at
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+from mani_skill.examples.motionplanning.noahbiarm.utils import get_3d_bbox, get_actor_obb
+from mani_skill.agents.robots import Panda
+
+from msx_envs.agent import PandaSphere
+from .scene_loader_builder import SceneLoaderBuilder
+
+
+@register_env("GraspCup-v1", max_episode_steps=500)
+class GraspCupV1Env(GraspBowlEnv):
+    def __init__(self, object_config_dir="base_configs/base_table_shelf.yaml", 
+                        create_obstacle_point_cloud=True, is_inference=False, **kwargs):
+        SUPPORTED_ROBOTS = ["panda", "panda_sphere"]
+        self.agent: Union[Panda, PandaSphere]
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.object_config_dir = object_config_dir
+        self.is_inference = is_inference
+        self.tcp_pose_valid = True
+        super().__init__(**kwargs)
+
+    def __initialize_episode_params(self):
+        super().__initialize_episode_params()
+        # Add any additional initialization for GraspBowlV1Env here
+    
+    def _get_obs_extra(self, info: Dict):
+        # check for grasping
+        self.grasped = max(self.grasped , self.agent.is_grasping(self.bowl))
+        # print("!!current grasped self.agent.is_grasping(self.bowl) is: ", self.agent.is_grasping(self.bowl))
+
+        if not self.grasped:
+            goal_pose = self.compute_grasp_pose()
+        else:
+            goal_pose = self.compute_release_pose()
+
+        if not self.is_inference:
+            # return extra observation, for training and data collection
+            obs = dict( #data collection setting
+                tcp_pose=self.agent.tcp.pose.raw_pose.detach().cpu().numpy(),
+                goal_pose=goal_pose.raw_pose.detach().cpu().numpy(),
+                goal_pos=goal_pose.p.detach().cpu().numpy(),
+                grasped=self.grasped,
+            )
+        else:
+            obs = dict( # inference setting
+                tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pose = goal_pose.raw_pose.detach().cpu().numpy().squeeze(0),
+                goal_pos  = goal_pose.p.detach().cpu().numpy().squeeze(0),
+                grasped=self.grasped,
+            )
+        if self.create_obstacle_point_cloud:
+            # print("obstacle_point_cloud is: ", self.obstacle_point_cloud.shape)
+            obs.update({
+                'obstacle_point_cloud': self.table_scene.obstacle_point_cloud
+            })
+        return obs
+    
+    def _load_scene(self, options: dict):
+        print("mesh dir: ", self.mesh_dir)
+        self.table_scene = SceneLoaderBuilder(
+            env=self,
+            object_config_dir=self.object_config_dir,
+            create_obstacle_point_cloud=self.create_obstacle_point_cloud,
+            keyframe="vertical_grasp",
+        )
+        self.table_scene.scene.sim_config.scene_config.solver_position_iterations = 25
+        #------- build table top -------#
+        max_iter = 10
+        for i in range(max_iter):
+            self._init_env_objects()  # init cup, rack, and goal site
+            try:
+                self.table_scene.build()
+                print(f"Table scene built successfully on attempt {i+1}")
+                break
+            except Exception as e:
+                print(f"Build attempt {i+1} failed: {e}")
+        else:
+            raise RuntimeError(f"Failed to build table scene after {max_iter} attempts")
+    
+    def _init_robot_pose(self):
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return sapien.Pose(p=[0, 0, 0])
+        else:
+            raise NotImplementedError
+    
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.grasped = 0
+        self.tcp_pose_valid = True
+        if self.robot_uids  in ["panda", "panda_wristcam", "panda_sphere"]:
+            return self._initialize_episode_panda(env_idx, options)
+        else:
+            raise NotImplementedError
+    
+    def evaluate(self):
+        BOWL_D = self.bowl_extents[0]
+        goal_extents = torch.from_numpy(self.goal_extents.copy())
+        goal_p, _ = self.get_goal_site_pose()
+        is_bowl_placed = (
+            self.is_inside(self.bowl.pose.p, goal_p , goal_extents)
+        )
+
+        euler = rotation_conversions.quaternion_to_euler(self.bowl.pose.q)
+        is_bowl_vertical = (
+            ((torch.abs(torch.abs(euler[:, 1])) - np.pi/2) <= np.pi/4 )&
+            ((torch.abs(torch.abs(euler[:, 2])) - np.pi/2) <= np.pi/4 )
+        ) 
+
+        # tcp pose
+        tcp_pose = self.agent.tcp.pose.raw_pose.detach().cpu().numpy()
+        if self.tcp_pose_valid and ( (tcp_pose[:, 0] < -0.2).all() or (tcp_pose[:, 2] > 0.65).all()):
+            print("tcp pose is out of boundary!, x axis is: ", tcp_pose[:, 0])
+            self.tcp_pose_valid = False
+
+        not_grasping = torch.logical_not(self.agent.is_grasping(self.bowl))
+
+        # plate is static
+        is_bowl_static = self.bowl.is_static()
+        
+        tcp_pose_valid_tensor = torch.tensor(self.tcp_pose_valid, device=is_bowl_placed.device)
+        success = torch.logical_and(is_bowl_placed, tcp_pose_valid_tensor)
+
+        return {
+            "success": success
+        }
+
+    
+    def _initialize_episode_panda(self, env_idx: torch.Tensor, options: dict):
+        print("-----Initialize episode panda------")
+        # self.rand_level = 2
+        print(f"rand level is: {self.rand_level}")
+        with torch.device(self.device):
+            b = len(env_idx)
+            self.table_scene.initialize(env_idx)
+
+            # get arm base positions
+            robot_p = self.agent.robot.pose.p
+            robot_q = self.agent.robot.pose.q
+
+            if self.rand_level >= 5:
+                side = torch.randint(0, 2, (b, 1)) * 2 - 1
+            else:
+                side = torch.ones((b, 1))
+
+            x_range = 0.15
+            y_range = 0.15
+            ##################################################################################
+            # cup pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.3
+            y = -1 * side * torch.ones((b, 1)) * 0.7
+            z = torch.ones((b, 1)) * 0.01
+
+            # default rotation
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 1] = np.pi # set cup upright
+            
+            # ---- Apply randomness by level ----
+            # default no jitter
+            device = self.device if hasattr(self, "device") else torch.device("cpu")
+            dtype = torch.float32
+            x_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+            y_rand = torch.zeros((b, 1), dtype=dtype, device=device)
+
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+            
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                roll_jit = torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+
+           # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b,3)
+            # xyz = torch.tensor([[0.3, -0.7, 0.0]])
+            p = robot_p + xyz                   # (b,3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b,4)  (check expected euler order)
+            self.bowl.set_pose(Pose.create_from_pq(p=p, q=q))
+
+            ##################################################################################
+            # rack pose initialize
+            # ---- Base position (relative) ----
+            x = torch.ones((b, 1)) * 0.25
+            y = 1 * side * torch.ones((b, 1)) * 0.55
+            z = torch.zeros((b, 1))
+
+            # default rotation (keep ZYX comment consistent with your conversion)
+            euler = torch.zeros((b, 3), dtype=torch.float32)
+            euler[:, 0] = np.pi  # roll
+
+            # default no jitter
+            x_rand = torch.zeros((b, 1), dtype=torch.float32)
+            y_rand = torch.zeros((b, 1), dtype=torch.float32)
+
+            # ---- Apply randomness by level ----
+            if self.rand_level == 1:
+                # small XY jitter (half range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range / 2, x_range / 2)
+                y_rand = torch.empty(b, 1).uniform_(-y_range / 2, y_range / 2)
+            elif self.rand_level >= 2:
+                # larger XY jitter (full range)
+                x_rand = torch.empty(b, 1).uniform_(-x_range, x_range)
+                y_rand = torch.empty(b, 1).uniform_(-y_range, y_range)
+
+            x = x + x_rand
+            y = y + y_rand
+
+            if self.rand_level >= 3:
+                # rotation jitter around roll (optionally add yaw jitter if needed)
+                roll_jit =  torch.empty(b, 1).uniform_(-np.pi/6, np.pi/6)
+                euler[:, 0] = euler[:, 0] + roll_jit
+                # Optional:
+                # euler[:, 2] = euler[:, 2] + torch.empty((b,), dtype=dtype, device=device).uniform_(-np.pi/12, np.pi/12)
+
+            # ---- Compose absolute pose ----
+            xyz = torch.cat((x, y, z), dim=1)   # (b, 3)
+            p = robot_p + xyz                   # (b, 3)
+            q = rotation_conversions.euler_to_quaternion(euler)  # (b, 4)
+                        
+            self.rack.set_pose(Pose.create_from_pq(p=p, q=q))
+            # gp, gq = self.get_goal_site_pose()
+            # self.goal_site.set_pose(Pose.create_from_pq(p=gp, q=gq))
+
+    def _init_env_objects(self):
+        """Initialize cup, rack, and goal site actors in the scene."""
+        # --- Cup (**Named as bowl**) ---
+        self.bowl = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/kalas_mug.obj"),
+            scale=(1, 1, 1),
+            color=np.array([0, 0, 255, 255], dtype=np.float32) / 255,
+            name="bowl",
+            body_type="dynamic",
+            add_collision=True,
+            initial_pose=sapien.Pose(p=[0, 0, 0], q=[1, 0, 0, 0]),
+        )
+        self.bowl_extents = get_actor_obb(self.bowl).extents
+
+        # --- Rack ---
+        self.rack = actors.build_nonconvex_from_mesh(
+            self.scene,
+            mesh_path=os.path.join(self.mesh_dir, "./assets/plates/stamling_rack.obj"),
+            scale=(1, 1, 1),
+            color=np.array([216, 215, 211, 255], dtype=np.float32) / 255,
+            name="rack",
+            body_type="dynamic",
+            initial_pose=sapien.Pose(p=[0, 0, 0]),
+        )
+        self.rack_extents = get_actor_obb(self.rack).extents
+
+        # --- Goal site ---
+        self.goal_site = actors.build_box(
+            self.scene,
+            half_sizes=[
+                self.rack_extents[0] / 2 / 4,
+                self.rack_extents[1] / 2 * 0.9,
+                self.rack_extents[2] / 2,
+            ],
+            color=[0, 1, 0, 0],
+            name="goal_site",
+            body_type="kinematic",
+            add_collision=False,
+            initial_pose=sapien.Pose(
+                p=[+self.rack_extents[0] * 9.5 / 14 / 2, 0, self.rack_extents[2] / 2]
+            ),
+        )
+        self.goal_extents = np.array(
+            [self.rack_extents[0] / 4, self.rack_extents[1] * 0.9, self.rack_extents[2]]
+        )
+    
+    @property
+    def _default_human_render_camera_configs(self):
+        # import pdb; pdb.set_trace()
+        if self.robot_uids in ["panda", "panda_wristcam", "panda_sphere"]:
+            # PANDA CAMERA
+            # pose = look_at([1.4, -1, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+            pose = look_at([2, 0, 1], [0.0, 0.0, 0.4],[0, 0.01, 1])
+        else:
+            raise NotImplementedError
+        
+        # import pdb; pdb.set_trace()
+        return CameraConfig("render_camera", pose, 1024, 1024, 1, 0.01, 100)
+
+
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_generator_env.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_generator_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..856bd610d707b3b201d220613e60df01c5c2a3ab
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_generator_env.py
@@ -0,0 +1,583 @@
+# original scene gen env, but customized for cup grasp task. Currently not working. Use grasp_cup_obstacles.py instead
+from collections import defaultdict
+from functools import partial
+import os
+import pathlib
+from typing import Callable, Final, Literal
+
+
+import numpy as np
+import json
+from sapien.physx import PhysxMaterial
+import sapien.core as sapien
+from transforms3d.euler import euler2quat
+from transforms3d.quaternions import qmult
+
+import yaml
+from mani_skill import format_path
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.utils.io_utils import load_json
+from mani_skill.utils.geometry import get_axis_aligned_bbox_for_actor
+from mani_skill.utils.registration import register_env
+from cfdp.utils.object_placement_2 import PlacementBounds, ObjectBoundingBox, find_valid_position_2d
+from mani_skill.utils.building.ground import build_ground
+
+from msx_envs import assets
+from msx_envs.legacy.random_obstacle_reach_env import RandomObstacleReachEnv
+
+from .scene_loader_builder import SceneLoaderBuilder
+
+
+OUT_OF_BOUNDS: Final = np.array([0, 0, -900])
+OBJECTS_KEY = 'objects'
+
+@register_env(
+    "SceneGenerator-v0",
+    max_episode_steps=1,
+)
+class SceneGeneratorEnv(RandomObstacleReachEnv):
+    """This environment generates actors at runtime and outputs the new scene to a YAML config.
+
+    This config can be retrieved in info['gen_scene'] on reset.
+    """
+    def __init__(
+        self,
+        *args, 
+        distractor_count: int = 5,
+        object_place_bounds: tuple[tuple[float, float], tuple[float, float], tuple[float]] | None = None,
+        asset_root=str(pathlib.Path(assets.__file__).parent),
+        base_object_config_path: str | pathlib.Path = "base_table.yaml",
+        model_config="asset_tables.yaml",
+        scene_config_serialization_mode: Literal["yaml", "json", "dict"] = "yaml",
+        distractor_rotation_axes: tuple[bool, bool, bool] = (False, False, True),
+        minimum_object_aabb_separation=0.05, # this can be below 0
+        table_scale: list[float, float] = [1.0, 1.0],
+        # extra parameters to run with test_ms3_envs.py
+        rand_level: int = 0,
+        object_config_dir: str | None = None,
+        **kwargs
+
+    ):
+        self.asset_root = asset_root
+        self.model_configs = model_config 
+        self.base_scene_config_path=base_object_config_path
+        self.table_scale = table_scale
+
+        model_db_path = self._get_asset_path(self.model_configs)
+        # Load object configurations before calling parent's __init__
+        with open(model_db_path, 'r') as f:
+            self.model_db = yaml.safe_load(f)
+
+        object_config_path = self._get_asset_path(self.base_scene_config_path)
+
+        # Load object configurations before calling parent's __init__
+        with open(object_config_path, 'r') as f:
+            self.base_object_configs = yaml.safe_load(f)
+        self.base_object_configs[OBJECTS_KEY] = _key_hierarchy_dict(self.base_object_configs[OBJECTS_KEY])
+
+        self._initial_actor_states = {}
+        self._distractor_count = distractor_count
+        self._object_place_bounds = object_place_bounds
+        self._minimum_object_aabb_separation = minimum_object_aabb_separation
+
+        self.scene_serializer_func: Callable[[str], dict]
+        if scene_config_serialization_mode == "json":
+            self.scene_serializer_func = partial(
+                json.dumps,
+                allow_nan=False,
+            )
+        elif scene_config_serialization_mode == "yaml":
+            self.scene_serializer_func = partial(
+                yaml.dump,
+                encoding='utf-8',
+                allow_unicode=True,
+                # sort_keys=False,
+                # default_flow_style=None,
+                # indent=4,
+                # width=120,
+            )
+        elif scene_config_serialization_mode == "dict":
+            # Just passthrough
+            self.scene_serializer_func = lambda d: d
+        else:
+            raise ValueError("Invalid serialization mode. Choose from 'json', 'yaml', or 'dict'")
+
+        self.new_distractors = []
+        self._generated_object_configs = {
+            OBJECTS_KEY: defaultdict(dict),
+        }
+        self._generated_object_configs[OBJECTS_KEY].update(self.base_object_configs[OBJECTS_KEY])
+        
+        self.ycb_objects: dict[str, sapien.ActorBase] = {}
+        # self.obstacle_actors: dict[str, sapien.ActorBase] = {}
+        self.obstacle_actors: dict[str, sapien.Entity] = {}
+        self.distractor_rotation_axes = distractor_rotation_axes
+
+        self.goal_pos = np.zeros(3)
+        self.goal_rot = np.array([1, 0, 0, 0])  # wxyz
+        self.tcp = None
+        self.goal_thresh = 0.01
+        self.min_goal_dist = 0.2
+
+        super().__init__(*args, **kwargs, 
+            # scene_builder = SceneLoaderBuilder(
+            #     env=self,
+            #     object_config_dir=object_config_dir,
+            #     create_obstacle_point_cloud=False,
+            #     keyframe="vertical_grasp",
+            #     )
+        )
+
+    def _get_asset_path(self, asset):
+        resolved_asset = format_path(asset)
+        return (
+            str(pathlib.Path(self.asset_root) / resolved_asset) 
+            if not os.path.isabs(asset) 
+            else resolved_asset
+        )
+    
+    def _settle(self, t):
+        # step the simulation and let the scene settle for t seconds
+        settle(self, t)
+
+    def _load_scene(self, options: dict):    
+        # Replace default ReplicaCAD builder
+        self.scene_builder = SceneLoaderBuilder(
+            env=self,
+            object_config_dir="base_configs/base_table_shelf.yaml",
+            create_obstacle_point_cloud=True,
+            keyframe="vertical_grasp",
+        )
+        self.scene_builder.build() # build scene and table
+        self._load_new_ycb_distractors() # load distractors
+        # self._load_actors()
+
+        self.tcp = self.agent.tcp
+
+    def _load_actors(self):
+        """Load actors only, state is set later in _initialize()."""
+        print("------------------------Loading actors------------------------")
+
+        self._add_ground()
+        self.goal_site = self._build_sphere_site(self.goal_thresh)
+        
+        # Clear previous obstacles
+        self.obstacles = []
+        all_points = []
+
+        self._load_base_scene_actors()
+        self._load_new_obstacles()
+        self._load_new_ycb_distractors()
+        
+        # Process each obstacle configuration
+        for obstacle_config in self.obstacle_configs:
+            obstacle_type = obstacle_config.get('type', 'box')
+            if obstacle_type == 'box':
+                obstacle, points = self._create_box_obstacle(obstacle_config)
+            elif obstacle_type == 'sphere':
+                obstacle, points = self._create_sphere_obstacle(obstacle_config)
+            elif obstacle_type == 'mesh':
+                obstacle, points = self._create_mesh_obstacle(obstacle_config)
+            else:
+                raise ValueError(f"Unsupported obstacle type: {obstacle_type}")
+            
+            self.obstacles.append(obstacle)
+            all_points.append(points)
+            
+        if all_points:
+            self.obstacle_point_cloud = np.vstack(all_points)
+
+    def _initialize_actors(self):
+        """Initialize the actor state."""
+        super()._initialize_actors()
+
+        self._initialize_base_scene_actors()
+        if self._object_place_bounds is None:
+            # self._discover_table_bounds()
+            self._scale_table_bounds(scales=self.table_scale)
+        self._initialize_new_scene_actors()
+
+    def _discover_table_bounds(self):
+        """Discover the bounds of the table."""
+        bbox_min, bbox_max = get_axis_aligned_bbox_for_actor(self.ycb_objects['table'])
+        self._object_place_bounds = [bbox_min[:2].tolist(), bbox_max[:2].tolist(), [2]]
+
+    def _scale_table_bounds(self, scales: list[float] = [1.0, 1.0]):
+        """Scale the table bounds by the given factors.
+        
+        Args:
+            scales: Scale factors [scale_x, scale_y] for x and y dimensions
+        """
+        scale_x, scale_y = scales
+        bbox_min, bbox_max = get_axis_aligned_bbox_for_actor(self.ycb_objects['table'])
+        center_x = (bbox_max[0] + bbox_min[0]) / 2
+        center_y = (bbox_max[1] + bbox_min[1]) / 2
+        
+        half_width = (bbox_max[0] - bbox_min[0]) / 2
+        half_length = (bbox_max[1] - bbox_min[1]) / 2
+        
+        scaled_min = [
+            center_x - half_width * scale_x,
+            center_y - half_length * scale_y
+        ]
+        scaled_max = [
+            center_x + half_width * scale_x,
+            center_y + half_length * scale_y
+        ]
+        
+        self._object_place_bounds = [scaled_min, scaled_max, [2]]
+
+    def _set_env_ycb_obj_attr(self, name, obj):
+        setattr(self, f"ycb_object_{name}", obj)
+        self.ycb_objects[name] = obj
+    
+    def _set_env_obstacle_obj_attr(self, name, obj):
+        setattr(self, f"obstacle_object_{name}", obj)
+        self.obstacle_actors[name] = obj
+
+    def _load_actor_helper(self,
+        scene: sapien.Scene,
+        scale: float | tuple[float, float, float] | None = None,
+        physical_material: PhysxMaterial = None,
+        density: float | None = None,
+        visual_mesh_path: str | None = None,
+        collision_mesh_path: str | None = None,
+    ):
+        """Load an actor from a model ID."""
+        builder = scene.create_actor_builder()
+
+        if isinstance(scale, (int, float)):
+            scale = (scale, scale, scale)
+        
+        if visual_mesh_path is not None:
+            obj_visual_path = self._get_asset_path(visual_mesh_path)
+            builder.add_visual_from_file(
+                filename=obj_visual_path, 
+                scale=scale
+            )
+        
+        obj_collision_path = self._get_asset_path(collision_mesh_path)
+        builder.add_multiple_convex_collisions_from_file(
+            filename=obj_collision_path,
+            scale=scale,
+            material=physical_material,
+            density=density or 1000,
+        )
+
+        return builder
+
+    def _load_base_scene_actors(self):
+        for name, obj_config in self.base_object_configs[OBJECTS_KEY].items():
+            builder = self._load_actor_helper(
+                scene=self.scene,
+                scale = obj_config.get('scale', [1.0]*3),
+                visual_mesh_path=obj_config.get('visual', {}).get('path'),
+                collision_mesh_path = obj_config.get('collision', {}).get('path'),
+            )
+            # Build actor as static or dynamic based on config
+            if obj_config.get('static', True):
+                obj = builder.build_static(name)
+            else:
+                obj = builder.build(name)
+
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            self._set_env_ycb_obj_attr(name, obj)
+
+    def _load_new_ycb_distractors(self):
+        ycb_asset_table = self.model_db['ycb']
+        model_keys = list(ycb_asset_table.keys())
+        self.new_distractors = [ # randomly choose _distractor_countdistractor keys
+            str(s) 
+            for s 
+            in self._episode_rng.choice(
+                model_keys, 
+                self._distractor_count, 
+                replace=False
+            )
+        ]
+
+        for name in self.new_distractors:
+            scale=ycb_asset_table[name].get('scale', 1.0)
+            visual_mesh_path=ycb_asset_table[name].get('visual_mesh_path')
+            collision_mesh_path=ycb_asset_table[name].get('collision_mesh_path')
+            builder = self._load_actor_helper(
+                scene=self.scene,
+                scale=scale,
+                visual_mesh_path=visual_mesh_path,
+                collision_mesh_path=collision_mesh_path,
+            )
+            obj = builder.build(name)
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            self._set_env_ycb_obj_attr(name, obj)
+
+            self._generated_object_configs[OBJECTS_KEY][name].update(
+                {
+                    'visual': {
+                        'path': visual_mesh_path,
+                    },
+                    'collision': {
+                        'path': collision_mesh_path,
+                    },
+                    'scale': scale,
+                }
+            )
+    
+    def _load_new_obstacles(self):
+        obstacle_asset_table = self.model_db['obstacles']
+        model_keys = list(obstacle_asset_table.keys())
+        self.new_obstacles = [str(s) for s in model_keys]
+
+        # scale: [0.6, 1, 0.7]
+        scale = [np.random.uniform(low=0.4, high=0.8),
+                 np.random.uniform(low=0.6, high=1.0),
+                 np.random.uniform(low=0.4, high=0.8)]
+
+        for name in self.new_obstacles:
+            scale=obstacle_asset_table[name].get('scale', scale)
+            visual_mesh_path=obstacle_asset_table[name].get('visual_mesh_path')
+            collision_mesh_path=obstacle_asset_table[name].get('collision_mesh_path')
+            builder = self._load_actor_helper(
+                scene=self.scene,
+                scale=scale,
+                visual_mesh_path=visual_mesh_path,
+                collision_mesh_path=collision_mesh_path,
+            )
+            obj = builder.build(name)
+            # Set these object somewhere far away
+            # obj.set_pose(sapien.Pose(OUT_OF_BOUNDS))
+            # self._set_env_ycb_obj_attr(name, obj)
+            self._set_env_obstacle_obj_attr(name, obj)
+
+            self._generated_object_configs[OBJECTS_KEY][name].update(
+                {
+                    'visual': {
+                        'path': visual_mesh_path,
+                    },
+                    'collision': {
+                        'path': collision_mesh_path,
+                    },
+                    'scale': scale,
+                }
+            )
+
+    def _initialize_base_scene_actors(self):
+        actors = {a.name: a for a in self.get_actors()}
+
+        for name, obj_config in self.base_object_configs[OBJECTS_KEY].items():
+            obj = actors.get(name)
+            assert isinstance(obj, (sapien.ActorBase, sapien.ActorStatic))
+            obj.set_pose(sapien.Pose(
+                obj_config['pose']['position'], 
+                obj_config['pose']['quaternion']
+            ))
+
+    def _initialize_new_scene_actors(self):
+        """Set the initial state of the newly generated objects."""
+        assert self._object_place_bounds is not None, f"Table bounds must be initialized."
+        placed_objects = [
+            (actor, ObjectBoundingBox(*(local_bbox_for_actor(actor=actor))))
+            for actor
+            in [self.ycb_objects[actor_name] for actor_name in self.base_object_configs[OBJECTS_KEY] if actor_name != 'table']
+        ]
+        placement_bounds=PlacementBounds(*self._object_place_bounds)
+
+        for actor_name in self.new_distractors:
+            actor = self.ycb_objects[actor_name]
+            actor.set_pose(sapien.Pose([100, 100, -50]))
+
+
+        placed_obstacles = [(actor, ObjectBoundingBox(*(local_bbox_for_actor(actor=actor)))) for actor in self.obstacle_actors.values()]
+        for obs_actor_name in self.new_obstacles:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            quat = [1, 0, 0, 0]
+            obs_actor.set_pose(sapien.Pose(obs_actor.pose.p, quat))
+            bb_min, bb_max = local_bbox_for_actor(actor=obs_actor)
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+            success = False
+            while not success:
+                valid_position_2d = find_valid_position_2d(
+                    placement_bounds=placement_bounds,
+                    actor=obs_actor,
+                    actor_local_bbox=bbox,
+                    placed_objects=placed_obstacles,
+                    rng=self._episode_rng,
+                    retries=5000, # worst case
+                    # minimum_aabb_separation=self._minimum_object_aabb_separation,
+                    minimum_aabb_separation=0.01,
+                )
+                z = (- bb_min[2])
+                valid_position = np.hstack([valid_position_2d, z])
+                print(f"Valid position1: {valid_position}")
+                obs_actor.set_pose(sapien.Pose(valid_position, quat))
+                self._settle_distractor(distractor_obj=obs_actor, motion_lock_axes=(1, 1, 0, 1, 1, 1))
+                if obs_actor.pose.p[2] > 0 and obs_actor.pose.p[2] < 0.4: # if z is unnormal resample it
+                    success = True
+                    
+        for actor_name in self.new_distractors:
+            actor = self.ycb_objects[actor_name]
+            euler = np.random.uniform(low=-np.pi/2, high=np.pi/2, size=(3,))
+            euler = np.multiply(euler, np.array(self.distractor_rotation_axes, dtype=np.int8))
+            quat = euler2quat(*euler)
+            actor.set_pose(sapien.Pose(actor.pose.p, quat))
+            bb_min, bb_max = local_bbox_for_actor(actor=actor)
+            bbox = ObjectBoundingBox(bb_min, bb_max)
+            valid_position_2d = find_valid_position_2d(
+                placement_bounds=placement_bounds,
+                actor=actor,
+                actor_local_bbox=bbox,
+                placed_objects=placed_objects,
+                rng=self._episode_rng,
+                retries=1000, # worst case
+                minimum_aabb_separation=self._minimum_object_aabb_separation,
+            )
+            ground_level = 0
+            # Move up by the min z of the bounding box
+            z = ground_level + (- bb_min[2])
+            # just be lazy and give a little more height
+            z += 0.05
+            # If there is less than 0 separation we may want objects to be more likely to stack
+            if self._minimum_object_aabb_separation < 0:
+                z += 0.3
+            # valid_position = self._find_valid_position(placed_objects, distractor_model_id, distractor_model_scale, self._distractor_pos_random, retries=100000)
+            if valid_position_2d is None:
+                print(f"Warning: Default to placing randomly. Could not find a valid position for distractor {actor_name} after maximum attempts.")
+                valid_position_2d = placement_bounds.random_in(rng=self._episode_rng)
+                ## HACK: add extra height to hopefully stack up
+                z += 0.3
+            valid_position = np.hstack([valid_position_2d, z])
+            # Find a valid position
+            actor.set_pose(sapien.Pose(valid_position, actor.pose.q))
+            self._settle_distractor(distractor_obj=actor)
+
+            # get updated AABB
+            final_bbox = ObjectBoundingBox(*(local_bbox_for_actor(actor=actor)))
+            placed_objects = placed_objects + [(actor, final_bbox)]
+
+        # Unlock motion
+        for actor_name, actor in self.ycb_objects.items():
+            if isinstance(actor, sapien.ActorStatic):
+                continue
+            actor.lock_motion(0, 0, 0, 0, 0, 0)
+            actor.set_velocity(np.zeros(3))
+            actor.set_angular_velocity(np.zeros(3))
+            self._settle(0.5)
+
+        lin_vel, ang_vel = 0.0, 0.0
+        for actor_name, actor in self.ycb_objects.items():
+            if isinstance(actor, sapien.ActorStatic):
+                continue
+            lin_vel += np.linalg.norm(actor.velocity)
+            ang_vel += np.linalg.norm(actor.angular_velocity)
+        if lin_vel > 1e-3 or ang_vel > 1e-2:
+            self._settle(1.5)
+        
+        for obs_actor_name in self.new_obstacles:
+            obs_actor = self.obstacle_actors[obs_actor_name]
+            print("pose at the end: ", obs_actor.pose)
+        
+
+    def _serialize_actors(self) -> dict:
+        """Serialize the actors to a hierarchy.
+
+        This is useful for saving and loading the scene state. Example:
+        ```yaml
+        # yaml
+        objects:
+            -   name: table
+                visual:
+                    path: object/table/desktable.glb
+                collision:
+                    path: "object/table/desktable.obj"  # same as visual in this case
+                    scale: [2.0, 1.0, 1.5]
+                    pose:
+                        position: [0.4, 0, -0.75]
+                        quaternion: [0, 0, 0.7071068, 0.7071068]
+        ```
+        """
+        actors_dict = self._generated_object_configs.copy()
+        for actor_name, actor in self.ycb_objects.items():
+            actors_dict[OBJECTS_KEY][actor_name].update({
+
+                'pose': {
+                    'position': actor.pose.p.tolist(),
+                    'quaternion': actor.pose.q.tolist(),
+                }
+            })
+
+        # convert back to list version
+        actors_dict[OBJECTS_KEY] = _list_hierarchy_dict(actors_dict[OBJECTS_KEY])
+
+        return self.scene_serializer_func(actors_dict)
+
+
+    def reset(self, seed=None, options=None):
+        obs, info = super().reset(seed, options)
+        info['scene_config'] = self._serialize_actors()
+        return obs, info
+    
+    def _settle_distractor(
+        self,
+        distractor_obj: sapien.Entity,
+        motion_lock_axes=(1, 1, 0, 0, 0, 0),
+    ):
+        return settle_distractor(
+            env=self, 
+            distractor_obj=distractor_obj, 
+            t=0.5,
+            drop_height=0.5,
+            unlock_at_end=False,
+            motion_lock_axes=motion_lock_axes,
+        )
+    
+    def _add_ground(self):
+        floor_width = 100
+        if self.scene.parallel_in_single_scene:
+            floor_width = 500
+        self.ground = build_ground(
+            self.scene, floor_width=floor_width, altitude=-0.8
+        )
+
+def _list_hierarchy_dict(dict_obj: dict):
+    return [{'name': k, **v} for k, v in dict_obj.items() if isinstance(v, dict)]
+
+def _key_hierarchy_dict(list_obj: list, on_key: str = 'name'):
+    return {v[on_key]: v for v in list_obj if v.get(on_key) is not None}
+
+
+def settle_distractor(
+    env: BaseEnv,
+    distractor_obj: sapien.Entity,
+    t: float,
+    drop_height: float = 0.5,
+    unlock_at_end: bool = False,
+    motion_lock_axes: tuple[int, int, int, int, int, int] = (1, 1, 0, 1, 1, 0),
+    up: np.ndarray = np.array([0., 0., 1.]),
+):
+    drop = up * drop_height
+    distractor_obj.set_pose(sapien.Pose(distractor_obj.pose.p + drop, distractor_obj.pose.q))
+    distractor_obj.set_velocity(np.zeros(3))
+    distractor_obj.set_angular_velocity(np.zeros(3))
+    # Lock x and y and pitch and roll
+    assert len(motion_lock_axes) == 6, f"`{motion_lock_axes=}` must be a tuple of length 6"
+    distractor_obj.lock_motion(*motion_lock_axes)
+
+    # Let distractor objects fall onto the table
+    settle(env, t)
+
+    if unlock_at_end:
+        distractor_obj.lock_motion(*[0]*6)
+
+
+def settle(env: BaseEnv, t):
+    # step the simulation and let the scene settle for t seconds
+    sim_steps = int(env.sim_freq * t)
+    for _ in range(sim_steps):
+        env._scene.step()
+
+def local_bbox_for_actor(actor: sapien.Entity) -> tuple[np.ndarray, np.ndarray]:
+    mins, maxs = get_axis_aligned_bbox_for_actor(actor)
+    mins -= actor.pose.p
+    maxs -= actor.pose.p
+    return mins, maxs
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_loader_builder.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_loader_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d80396278430c982f2afd88c60436662a21e0a3
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/ms3/scene_loader_builder.py
@@ -0,0 +1,415 @@
+import os
+import os.path as osp
+import glob
+from pathlib import Path
+from typing import List
+import yaml
+import trimesh
+
+import numpy as np
+import sapien
+import sapien.render
+import torch
+from transforms3d.euler import euler2quat
+
+from mani_skill.agents.multi_agent import MultiAgent
+from mani_skill.agents.robots.fetch import FETCH_WHEELS_COLLISION_BIT
+from mani_skill.utils.building.ground import build_ground
+from mani_skill.utils.scene_builder import SceneBuilder
+from mani_skill.utils.structs.pose import Pose
+
+from contextlib import contextmanager
+from cfdp.utils.advanced_kinematics_solver import AdvancedKinematicsSolver
+
+from msx_envs import PACKAGE_ASSETS_DIR
+from msx_envs import ENV_CONFIG_DIR
+
+# TODO (stao): make the build and initialize api consistent with other scenes
+class SceneLoaderBuilder(SceneBuilder):
+    '''
+    Load scene from yaml config file
+    '''
+    def __init__(self, env, 
+                object_config_dir=None, 
+                obstacle_configs=None,
+                create_obstacle_point_cloud=True, 
+                keyframe="rest"):
+        self.keyframe = keyframe
+        super().__init__(env)
+        self.kinematic_solver = AdvancedKinematicsSolver(env.agent.robot)
+        # Set these attributes before calling super().__init__
+        self.create_obstacle_point_cloud = create_obstacle_point_cloud
+        self.obstacle_configs = obstacle_configs if obstacle_configs else []
+        self.obstacle_point_cloud = None
+        self.object_path_prefix = PACKAGE_ASSETS_DIR
+        
+        # Get all yaml files from object_config_path
+        self.object_config_files = []
+        if object_config_dir:
+            object_config_path = osp.join(ENV_CONFIG_DIR, object_config_dir)
+
+            if osp.isfile(object_config_path):
+                # Case 1: user passed a single config file
+                if object_config_path.endswith(('.yaml', '.yml')):
+                    self.object_config_files.append(object_config_path)
+            elif osp.isdir(object_config_path):
+                # Case 2: user passed a directory
+                yaml_files = glob.glob(osp.join(object_config_path, '*.y*ml'))
+                self.object_config_files.extend(yaml_files)
+            else:
+                raise FileNotFoundError(f"Config path not found: {object_config_path}")
+        # print("object config files: ", self.object_config_files)
+        self.current_config_index = 0  # Add counter
+        self.obstacles = []
+        self.primary_objects_ids = []
+
+    def build(self):
+        self._load_actors()
+
+        # builder = self.scene.create_actor_builder()
+        # model_dir = osp.join(PACKAGE_ASSETS_DIR, "table")
+        # table_model_file = osp.join(model_dir, "table.glb")
+        # scale = 1.75
+        # table_pose = sapien.Pose(q=euler2quat(0, 0, np.pi / 2))
+        # #Pose([0, 0, 0], [0.707107, 0, 0, 0.707107])
+        # # builder.add_nonconvex_collision_from_file(
+        # #     filename=table_model_file,
+        # #     scale=[scale] * 3,
+        # #     pose=table_pose,
+        # # )
+        # builder.add_box_collision(
+        #     pose=sapien.Pose(p=[0, 0, 0.9196429 / 2]),
+        #     half_size=(2.418 / 2, 1.209 / 2, 0.9196429 / 2),
+        # )
+        # builder.add_visual_from_file(
+        #     filename=table_model_file, scale=[scale] * 3, pose=table_pose
+        # )
+        # builder.initial_pose = sapien.Pose(
+        #     p=[-0.12 + 0.615, 0, -0.9196429], q=euler2quat(0, 0, np.pi / 2)
+        # )
+        # table = builder.build_kinematic(name="table-workspace")
+        # aabb = (
+        #     table._objs[0]
+        #     .find_component_by_type(sapien.render.RenderBodyComponent)
+        #     .compute_global_aabb_tight()
+        # )
+        # self.table_length = aabb[1, 0] - aabb[0, 0]
+        # self.table_width = aabb[1, 1] - aabb[0, 1]
+        # self.table_height = aabb[1, 2] - aabb[0, 2]
+        # floor_width = 100
+        # if self.scene.parallel_in_single_scene:
+        #     floor_width = 500
+        # self.ground = build_ground(
+        #     self.scene, floor_width=floor_width, altitude=-self.table_height
+        # )
+        # self.table = table
+        # self.scene_objects: List[sapien.Entity] = [self.table, self.ground]
+
+    def initialize(self, env_idx: torch.Tensor):
+        # table_height = 0.9196429
+        b = len(env_idx)
+        # self.table.set_pose(
+        #     sapien.Pose(p=[-0.12 + 0.615, 0, -0.9196429], q=euler2quat(0, 0, np.pi / 2))
+        # )
+        if str(self.env.robot_uids).startswith("panda"):
+            if not self.initialize_panda(): # Handle initialization failure
+                # print("Failed to initialize panda robot")
+                return False
+        else:
+            raise NotImplementedError("Robot type {} not supported".format(self.env.robot_uids))
+        return True
+    
+    def initialize_panda(self, max_iter=10):
+        ## IK
+        initial_qpos = np.array(
+            # initial pose
+            [0.08, -1.08, -0.27, -2.70, -0.24, 1.63, 0.75, 0.4, 0.4]
+            # v2, artificial to prevent screw motion in grasp cup
+            # [-1.52, 0.0, 1.615, -2.4, 2.89, 2.08, 0.966, 0.1, 0.1]
+        )
+        joint_states = None
+        found_valid_start = False
+        iter_count = 0
+        while not found_valid_start and iter_count < max_iter:
+            ##### RANDOM ENVIRONMENT #####
+            start_x = self.env._episode_rng.uniform(0.2, 0.5)
+            start_y = self.env._episode_rng.uniform(0.0, 0.5)
+            start_z = self.env._episode_rng.uniform(0.25, 0.6) # for full observation
+            #inference
+            # start_x = self.env._episode_rng.uniform(0.2, 0.5)
+            # start_y = self.env._episode_rng.uniform(0.4, 0.5)
+            # start_z = self.env._episode_rng.uniform(0.25, 0.3) # for full observation
+
+            start_pos = np.hstack([start_x, start_y, start_z])
+            # Create a sapien.Pose object using the position (keep default orientation)
+            start_pose = sapien.Pose(start_pos, [0, 1, 0, 0])
+            
+            found_valid_start, joint_states = self._check_robot_pose_valid(start_pose, initial_qpos)
+            if found_valid_start:
+                # print("found valid start pose: ", start_pose)
+                qpos = joint_states
+                # self.env.agent.reset(initial_qpos) # reset to initial pose first
+                self.env.agent.reset(qpos)
+                break
+            iter_count += 1
+        
+        self.env.agent.robot.set_pose(sapien.Pose([0, 0, 0])) # set base pose
+
+        if not found_valid_start:
+            # print("Failed to find valid start pose with iter_count {}".format(max_iter))
+            return False
+        
+        return True
+
+    def _load_actors(self):
+        # # print("ACTORS LOADEDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD")
+        floor_width = 100
+        if self.scene.parallel_in_single_scene:
+            floor_width = 500
+        self.ground = build_ground(
+            self.scene, floor_width=floor_width, altitude=-0.8
+        )
+        
+        # Clear previous obstacles
+        self.obstacles = []
+        obstacle_points = []  # Rename for clarity
+        self.primary_objects_ids = []
+        
+        # Create YCB objects from config first
+        object_configs = self.get_next_object_config()
+        ycb_points = self._create_ycb_objects(object_configs)  # Capture returned points
+        
+        # Process each obstacle configuration
+        for obstacle_config in self.obstacle_configs:
+            obstacle_type = obstacle_config.get('type', 'box')
+            if obstacle_type == 'box':
+                obstacle, points = self._create_box_obstacle(obstacle_config)
+            elif obstacle_type == 'sphere':
+                obstacle, points = self._create_sphere_obstacle(obstacle_config)
+            elif obstacle_type == 'mesh':
+                obstacle, points = self._create_mesh_obstacle(obstacle_config)
+            else:
+                raise ValueError(f"Unsupported obstacle type: {obstacle_type}")
+            
+            self.obstacles.append(obstacle)
+            obstacle_points.append(points)
+        
+        # Combine both sources of point clouds
+        point_cloud_sources = []
+        if obstacle_points:
+            point_cloud_sources.append(np.vstack(obstacle_points))
+        if ycb_points is not None:
+            point_cloud_sources.append(ycb_points)
+            
+        if point_cloud_sources:
+            self.obstacle_point_cloud = np.vstack(point_cloud_sources)
+        else:
+            self.obstacle_point_cloud = np.array([])
+
+    def get_next_object_config(self):
+        """Load the next object configuration file and cycle back to the beginning if at the end."""
+        if not self.object_config_files:
+            raise ValueError("No object configuration files found")
+            
+        with open(self.object_config_files[self.current_config_index], 'r') as f:
+            config = yaml.safe_load(f)
+            # print("file name: ", self.object_config_files[self.current_config_index])
+         
+        # Update index for next time, cycling back to 0 if we reach the end
+        self.current_config_index = (self.current_config_index + 1) % len(self.object_config_files)
+        return config
+    
+    def _create_ycb_objects(self, object_configs):
+        self.ycb_objects = {}
+        all_ycb_points = []
+        self.max_object_height = 0
+        for obj_config in object_configs['objects']:
+            builder = self.scene.create_actor_builder()
+            
+            # Handle scale that can be either float or list
+            scale = obj_config['scale']
+            if isinstance(scale, (int, float)):
+                scale = [float(scale)] * 3
+            
+            # Add visual mesh if specified
+            if 'visual' in obj_config:
+                builder.add_visual_from_file(
+                    filename=osp.join(self.object_path_prefix, obj_config['visual']['path']),
+                    scale=scale
+                )
+            
+            # Add collision mesh (required)
+            collision_config = obj_config['collision']
+            collision_path = osp.join(self.object_path_prefix, collision_config['path'])
+            
+            # Check if the path is a directory containing multiple obj files
+            if osp.isdir(collision_path):
+                collision_paths = [osp.join(collision_path, f) for f in os.listdir(collision_path) 
+                                  if f.endswith('.obj')]
+            else:
+                collision_paths = [collision_path]
+            
+            # Create point cloud using trimesh if required
+            if self.create_obstacle_point_cloud:
+                for collision_path in collision_paths:
+                    points = self._create_point_cloud_from_mesh(collision_path, scale, obj_config)
+                    # # print("object name: ", obj_config['name'])
+                    # # print("object points size: ", points.shape)
+                    all_ycb_points.append(points)  # Append transformed points
+            
+            ###################
+            for collision_path in collision_paths:
+                builder.add_multiple_convex_collisions_from_file(
+                    filename=collision_path,
+                    scale=scale,
+                    material=None,
+                    density=collision_config.get('density', 1000),
+                )
+            ###################
+            
+            # Build actor as static or dynamic based on config
+            if obj_config.get('static', True):
+                obj = builder.build_static(obj_config['name'])
+                # print(obj_config['name'], " is static")
+            else:
+                obj = builder.build(obj_config['name'])
+                # print(obj_config['name'], " is dynamic")
+            
+            # Set pose from config
+            obj.set_pose(sapien.Pose(
+                p = obj_config['pose']['position'],
+                q = obj_config['pose']['quaternion']
+            ))
+            
+            if obj.name == "table": # TODO, send this by variable
+                self.primary_objects_ids.append((obj.per_scene_id).item()) #or obj._bodies[0].entity.global_id; 
+
+            # Store reference to object and its point cloud
+            setattr(self, f"ycb_object_{obj_config['name']}", obj)
+            self.ycb_objects[obj_config['name']] = {
+                'actor': obj,
+                'points': points,
+                'position': np.array(obj_config['pose']['position']),
+                'quaternion': np.array(obj_config['pose']['quaternion'])
+            }
+        
+        # Return the point cloud instead of setting it directly
+        if all_ycb_points:
+            return np.vstack(all_ycb_points)
+        return None
+    
+    def _create_point_cloud_from_mesh(self, mesh_path, scale, obj_config):
+        # Load the mesh and get the geometry if it's a scene
+        loaded = trimesh.load(mesh_path)
+        if isinstance(loaded, trimesh.Scene):
+            # Get the first mesh from the scene
+            mesh = next(iter(loaded.geometry.values()))
+        else:
+            mesh = loaded
+        
+        # Scale the mesh if needed
+        if not np.allclose(scale, 1.0):
+            mesh.apply_scale(scale)
+        # Sample points from the mesh surface
+        # points, _ = trimesh.sample.sample_surface(mesh, 200)  # Sample points from mesh surface
+        # volume = mesh.volume
+        bbox = mesh.bounding_box
+        volume = bbox.volume
+        n_points = max(900, int(volume * 10000))            
+
+        # Sample surface points
+        surface_points, _ = trimesh.sample.sample_surface(mesh, n_points)
+        points = surface_points
+        
+        # Transform points to world coordinates using object's pose
+        obj_pos = np.array(obj_config['pose']['position'])
+        obj_quat = np.array(obj_config['pose']['quaternion'])
+        # Create transformation matrix
+        transform = trimesh.transformations.quaternion_matrix(obj_quat)
+        transform[:3, 3] = obj_pos
+        # Apply transformation to points
+        points = trimesh.transform_points(points, transform)
+        
+        ### debug for max object height
+        # Get the maximum z-coordinate from the transformed points
+        max_z = np.max(points[:, 2]) if points.size > 0 else 0
+        total_height = max_z  # Store the maximum height of this object
+        self.max_object_height = max(self.max_object_height, total_height)
+        # # print("object height: ", total_height)
+        return points
+
+    def _check_robot_pose_valid(self, robot_pose, initial_qpos, current_qpos = None):
+        # get controller and ik from agent
+        robot_pose_valid = False
+        joint_states = self.kinematic_solver.compute_ik(robot_pose, 
+                                                    initial_qpos=initial_qpos)
+        # with self.use_ee_controller() as ee_controller:
+        #     # print("type of controller: ", type(ee_controller))
+        #     joint_states = self.kinematic_solver.compute_ik(Pose.create(robot_pose), 
+        #                                             q0=initial_qpos)
+        if joint_states is not None:
+            # print("start joint states before collision checking: ", joint_states)
+            qpos = joint_states
+            # qpos = np.concatenate([joint_states, np.array([0.04, 0.04])])
+            self.env.agent.reset(qpos)# warning: potential issue here, we change the joint state of the agent
+            self.env.scene.step()
+            collided = self._evaluate_collided(primary_objects_ids = self.primary_objects_ids)
+            if collided is None:
+                robot_pose_valid = True
+                # print("start_pos: ", robot_pose)
+        if current_qpos is not None:
+            self.env.agent.reset(current_qpos)# change it back to the original qpos
+        else:
+            self.env.agent.reset(initial_qpos)# change it back to the original qpos
+        self.env.scene.step()
+        return (robot_pose_valid, joint_states if robot_pose_valid else None)
+
+    def _evaluate_collided(self, primary_objects_ids: list[int]):
+        """Acertain if the robot gripper collided with another object that is not one of the targets."""
+        def robot_actor_contact_info(robot_id_and_primary_object_blacklist: set[str], 
+                                     robot_links,
+                                     contact: sapien.pysapien.physx.PhysxContact):
+            for robot_link in robot_links:
+                # Get collision between the robot and an object that is not the robot or a primary manipulation object.
+                collided = (
+                    (robot_link == contact.bodies[0].entity.global_id or robot_link == contact.bodies[1].entity.global_id)
+                    and (
+                        (contact_obj := contact.bodies[0]).entity.global_id not in robot_id_and_primary_object_blacklist
+                        or (contact_obj := contact.bodies[1]).entity.global_id not in robot_id_and_primary_object_blacklist
+                    )
+                )
+                if collided:
+                    # print("collided: ", collided, "robot_link: ", robot_link, "contact_obj: ", contact_obj.get_name(), 'contact: ', contact)
+                    return collided, robot_link, contact_obj.get_name()
+            return False, None, None
+            
+        # contact pairs
+        contacts = self.scene.get_contacts()
+        # blacklist of objects that the robot should not check against
+        robot_link_ids = []
+        checked_robot_links = []
+        links = self.env.agent.robot.get_links()
+        for link in links:
+            for body in link._bodies:
+                robot_link_ids.append(body.entity.global_id)
+                if "wheel" not in link.name:
+                    checked_robot_links.append(body.entity.global_id)
+        blacklist = set(robot_link_ids)
+        blacklist.update(primary_objects_ids)
+        # robot links filtering out the links that are the robot's connection with the arena floor
+        checked_robot_links = set(checked_robot_links)
+        for contact in contacts:
+            collided, robot_link, obj_id = robot_actor_contact_info(blacklist, checked_robot_links, contact)
+            if collided:
+                return f"{obj_id}:{robot_link}"
+        return None
+    
+    @contextmanager
+    def use_ee_controller(self):
+        if self.env._control_mode != "pd_ee_target_delta_pos":
+            self.env.agent.set_control_mode("pd_ee_target_delta_pos")
+        ee_controller = self.env.agent.controller.controllers['arm']
+        try:
+            yield ee_controller
+        finally:
+            self.env.agent.set_control_mode(self.env._control_mode)
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d1c37db39a5d0c098f7d2a22d12b4f726a19e02
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/__init__.py
@@ -0,0 +1,3 @@
+from .simple_reach import solve as solveSimpleReach
+from .grasp_cup_testv3 import solve as solveGraspCup
+from .pick_box import solve as SolveBox
\ No newline at end of file
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diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_bowl_v0.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_bowl_v0.py
new file mode 100644
index 0000000000000000000000000000000000000000..06b5807f8eeec5481e75a229fe3fdf6d3b75325b
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_bowl_v0.py
@@ -0,0 +1,140 @@
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+import time
+import random
+from transforms3d.euler import euler2quat
+
+from mani_skill.envs.tasks import GraspBowlEnv
+from mani_skill.examples.motionplanning.panda.motionplanner import PandaArmMotionPlanningSolver
+from mani_skill.examples.motionplanning.panda.utils import compute_grasp_info_by_obb, get_actor_obb
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+
+def main():
+    env: GraspBowlEnv = gym.make(
+        "GraspBowl-v0",
+        obs_mode="none",
+        control_mode="pd_joint_pos",
+        render_mode="human",
+        reward_mode="dense",
+    )
+    
+    # Wrap the environment with RecordVideo
+    env = gym.wrappers.RecordVideo(env, video_folder="./videos", episode_trigger=lambda x: True)
+
+    for seed in range(100):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: GraspBowlEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+    #print("Debug")
+
+    # Check collision shapes
+    # #print(f"Debug: Bowl collision shapes: {env.unwrapped.bowl.get_collision_shapes()}")
+    # #print(f"Debug: Rack collision shapes: {env.unwrapped.rack.get_collision_shapes()}")
+
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )
+    
+    env = env.unwrapped
+    FINGER_LENGTH = 0.025
+    BOWL_D = env.bowl_extents[0]
+    BOWL_Z = env.bowl_extents[2]
+    RACK_Z = env.rack_extents[2]
+    ENV_Z_OFFSET = BOWL_Z + RACK_Z + FINGER_LENGTH
+
+    obb = get_actor_obb(env.bowl)
+ 
+    approaching = np.array([0, 0, -1], dtype=np.float32)
+    target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy().astype(np.float32)
+
+    grasp_info = compute_grasp_info_by_obb(
+        obb,
+        approaching=approaching,
+        target_closing=target_closing,
+        depth=FINGER_LENGTH
+    )
+    closing, center = grasp_info["closing"], grasp_info["center"]
+ 
+    grasp_pose = env.agent.build_grasp_pose(approaching, closing, env.bowl.pose.p)
+    euler = rotation_conversions.quaternion_to_euler(torch.tensor(grasp_pose.q).reshape(1, -1))
+    euler[:, 0] = 0
+    grasp_q = np.array(rotation_conversions.euler_to_quaternion(euler)).reshape(-1)
+
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p + [random.uniform(-0.05, 0.05), BOWL_D*0.28+random.uniform(-0.05, 0.05), BOWL_Z * random.uniform(4.0, 5.0)], q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to reach pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Grasp
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p + [0, BOWL_D*0.28, BOWL_Z*0.0], q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+        
+    planner.close_gripper()
+
+    # -------------------------------------------------------------------------- #
+    # Lift
+    # -------------------------------------------------------------------------- #
+    lift_pose = Pose.create_from_pq(p=grasp_pose.p + [random.uniform(-0.1, 0.1), BOWL_D*0.28 + random.uniform(-0.1, 0.1) , BOWL_Z* random.uniform(2.0, 5.0)], q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(lift_pose)
+    if res == -1: 
+        print("Failed to lift pose")
+        return res
+
+    # lift_pose = Pose.create_from_pq(p=grasp_pose.p + [0, BOWL_D*0.28, 1.5*ENV_Z_OFFSET], q=grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(lift_pose)
+    # if res == -1: 
+    #     print("Failed to lift pose")
+    #     return res
+
+    # # -------------------------------------------------------------------------- #
+    # # Hover over goalsite (rack pose)
+    # # -------------------------------------------------------------------------- #
+    p, q = env.get_goal_site_pose()
+    pose = Pose.create_from_pq(p=p, q=q)
+    hover_pose = sapien.Pose(pose.sp.p + [random.uniform(-0.1, 0.1), BOWL_Z+random.uniform(-0.1, 0.1), BOWL_Z*random.uniform(2.0, 4.0)], grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(hover_pose)
+    if res == -1: 
+        print("Failed to hover pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Lower & Release
+    # -------------------------------------------------------------------------- #
+    lower_pose = sapien.Pose(pose.sp.p + [0, BOWL_Z, BOWL_Z/10], grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(lower_pose)
+    if res == -1: 
+        print("Failed to lower pose")
+        return res
+
+    planner.open_gripper()
+   
+    return res
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_test.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d669a91bcb5b2d765334e4ca37d56268f5c3329
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_test.py
@@ -0,0 +1,406 @@
+# intermediate + constrained movements, but movement is twisty and weird
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+import time
+import random
+from transforms3d.euler import euler2quat
+
+import msx_envs.ms3.grasp_cup_v1
+from mani_skill.envs.tasks import GraspBowlEnv
+from mani_skill.examples.motionplanning.panda.motionplanner import PandaArmMotionPlanningSolver
+from mani_skill.examples.motionplanning.panda.utils import compute_grasp_info_by_obb, get_actor_obb
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+
+def main():
+    env: GraspCupV1Env = gym.make(
+        "GraspCup-v1",
+        obs_mode="state_dict",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        reward_mode="dense",
+        robot_uids="panda"
+    )
+    
+    # Wrap the environment with RecordVideo
+    # env = gym.wrappers.RecordVideo(env, video_folder="./videos", episode_trigger=lambda x: True)
+
+    for seed in range(100):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: GraspBowlEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+    #print("Debug")
+
+    # Check collision shapes
+    # #print(f"Debug: Bowl collision shapes: {env.unwrapped.bowl.get_collision_shapes()}")
+    # #print(f"Debug: Rack collision shapes: {env.unwrapped.rack.get_collision_shapes()}")
+
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )   
+
+    pc, res = _get_downsampled_obstacle_pc(env, voxel=0.02)
+    planner.planner.update_point_cloud(pc, resolution=res)
+
+    env = env.unwrapped
+    FINGER_LENGTH = 0.025
+    BOWL_D = env.bowl_extents[0]
+    BOWL_Z = env.bowl_extents[2]
+    RACK_Z = env.rack_extents[2]
+    ENV_Z_OFFSET = BOWL_Z + RACK_Z
+
+    obb = get_actor_obb(env.bowl)
+
+    #--------------------------------------------------------------------------- #
+    # Constraint: limit EEF tilt angle from vertical (world +Z)
+    #--------------------------------------------------------------------------- #
+    # --- Upright constraint for CRRT (keep EEF +Z aligned with world +Z) ---
+    TILT_DEG = 0  # set e.g. 2.0 for ≤2° tilt allowance
+    COS_T = float(np.cos(np.deg2rad(TILT_DEG)))
+    EZ = np.array([0.0, 0.0, 1.0], dtype=np.float64)
+
+    _mplib = planner.planner                       # MPLib wrapper
+    _pin   = _mplib.pinocchio_model                # Pinocchio model wrapper
+    move_link_idx = _mplib.link_name_2_idx[_mplib.move_group]
+    move_group_joint_indices = _mplib.move_group_joint_indices  # indices for the move group (Ndof)
+
+    def _eef_z_from_q(q_movegroup: np.ndarray) -> np.ndarray:
+        """
+        Return EEF local +Z axis in world frame for the given move-group q.
+        Uses the same API your solver already uses: forward kinematics + get_link_pose.
+        """
+        # Expand to full robot configuration expected by Pinocchio
+        full_q = _mplib.pad_move_group_qpos(q_movegroup)  # shape (Nfull,)
+        # Forward kinematics at that config
+        _pin.compute_forward_kinematics(full_q)
+        new_pose = _pin.get_link_pose(move_link_idx)      # has .q quaternion
+        # Quaternion -> rotation matrix (ManiSkill util; returns torch tensor (3,3))
+        R = rotation_conversions.quaternion_to_matrix(torch.tensor(new_pose.q, dtype=torch.float32))
+        z_eef = (-R[:, 0]).cpu().numpy().astype(np.float64).reshape(-1)
+        z_eef /= (np.linalg.norm(z_eef) + 1e-12)
+        return z_eef
+
+    def f(self_, x, out):
+        """
+        Equality constraint: c(x) = z_eef(x)·EZ - cos(theta_max) = 0
+        For strictly upright: theta_max = 0 => cos = 1.
+        """
+        z_eef = _eef_z_from_q(x)
+        out[0] = float(z_eef.dot(EZ) - COS_T)
+
+    def j(self_, x, out):
+        """
+        Jacobian dc/dq. Using: d/dq_i (z·EZ) = ((ω_i × z) · EZ),
+        where ω_i is the angular part of the 6D geometric Jacobian at EEF.
+        """
+        # Get rotational Jacobian (3 x Ndof) for the move group at q=x
+        full_q = _mplib.pad_move_group_qpos(x)
+        J6 = _pin.compute_single_link_jacobian(full_q, move_link_idx)  # (6, Nfull)
+        rotJ = np.asarray(J6[3:, move_group_joint_indices], dtype=np.float64)  # angular rows
+
+        z_eef = _eef_z_from_q(x)
+        Ndof = rotJ.shape[1]
+
+        # MPLib provides `out` as (1, Ndof); handle both 1D/2D defensively
+        if out.ndim == 1:
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+        else:  # shape (1, Ndof)
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[0, i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+    #--------------------------------------------------------------------------- #
+    #--------------------------------------------------------------------------- #
+
+        
+    # approaching = np.array([0, 0, -1], dtype=np.float32)
+    # target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy().astype(np.float32)
+
+    #--------------------------------------------------------------------------- #
+    # Compute horizontal grasp pose
+    #--------------------------------------------------------------------------- #
+    # --- Robustly get 3D positions from T matrices ---
+    T_tcp  = env.agent.tcp.pose.to_transformation_matrix()
+    T_bowl = env.bowl.pose.to_transformation_matrix()
+
+    # Handle batched vs non-batched; ensure numpy (3,)
+    tcp_p  = (T_tcp[0, :3, 3]  if T_tcp.ndim == 3  else T_tcp[:3, 3]).detach().cpu().numpy().astype(np.float32)
+    bowl_p = (T_bowl[0, :3, 3] if T_bowl.ndim == 3 else T_bowl[:3, 3]).detach().cpu().numpy().astype(np.float32)
+
+    half_h = 0.5 * env.bowl_extents[2]          # or from OBB: 0.5 * obb.extents[2]
+    center = bowl_p.copy()
+    center[2] += half_h*1.5                          # aim at mid-height
+
+    # --- Horizontal approach in XY ---
+    vec_xy = center - tcp_p            # (3,)
+    vec_xy[2] = 0.0                    # zero Z safely now
+    norm = np.linalg.norm(vec_xy)
+    vec_xy[2] = 0.0
+    approaching = vec_xy / (np.linalg.norm(vec_xy) + 1e-9)
+
+    # Closing axis: z_up × approaching  (also horizontal)
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+    target_closing = np.cross(z_up, approaching)
+    n = np.linalg.norm(target_closing)
+    target_closing = (target_closing / n) if n > 1e-6 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+
+
+    grasp_info = compute_grasp_info_by_obb(
+        obb,
+        approaching=approaching,
+        target_closing=target_closing,
+        depth=FINGER_LENGTH
+    )
+    closing = grasp_info["closing"]
+ 
+    grasp_pose = env.agent.build_grasp_pose(approaching, closing, center)
+    grasp_q = make_valid_xyzw_quat(grasp_pose.q) # ensure valid
+
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    pre_back  = env.bowl_extents[0] * 0.30
+    target_z  = center[2] + 0.015
+    reach_pos = grasp_pose.p - approaching * pre_back
+    reach_pos[2] = target_z
+    reach_pose = Pose.create_from_pq(p=reach_pos, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to reach pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Grasp
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+        
+    planner.close_gripper()
+
+    # -------------------------------------------------------------------------- #
+    # Lift
+    # -------------------------------------------------------------------------- #
+    lift_p  = grasp_pose.p + np.array([0.0, 0.0, 0.05], dtype=np.float32)
+    lift    = Pose.create_from_pq(p=lift_p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(lift)
+    if res == -1:
+        print("Failed to lift pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Compute HOVER target (position + upright orientation)
+    # -------------------------------------------------------------------------- #
+    CLEAR_Z = 0.15
+    p_goal, _ = env.get_goal_site_pose()
+    p_goal_v = np.asarray(p_goal, dtype=np.float32).squeeze()[:3]
+
+    hover_p = p_goal_v.copy()
+    hover_p[:2] += np.random.uniform(-0.03, 0.03, size=2).astype(np.float32)
+    hover_p[2]  += CLEAR_Z
+
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+    dir_xy = (hover_p - lift_p).astype(np.float32); dir_xy[2] = 0.0
+    n = np.linalg.norm(dir_xy)
+    approach_xy = (dir_xy / (n + 1e-9)) if n > 1e-9 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    target_closing = np.cross(z_up, approach_xy)
+    nc = np.linalg.norm(target_closing)
+    target_closing = (target_closing / (nc + 1e-9)) if nc > 1e-9 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+
+    tmp = env.agent.build_grasp_pose(approach_xy, target_closing, hover_p)
+    hover_q = np.asarray(tmp.q, dtype=np.float32).reshape(-1)
+    hover_q /= (np.linalg.norm(hover_q) + 1e-9)
+
+    # Optional sanity checks
+    assert np.all(np.isfinite(hover_p)); assert np.all(np.isfinite(hover_q))
+    assert abs(np.linalg.norm(hover_q) - 1.0) < 1e-3
+
+    # -------------------------------------------------------------------------- #
+    # Three mid waypoints (mid1, mid2, mid3), all upright
+    # -------------------------------------------------------------------------- #
+    # Common stuff
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+
+    # "0.5 out" in X (half of the lift->hover delta in X)
+    mid_x = float(lift_p[0] + 0.5 * (hover_p[0] - lift_p[0]))
+
+    # Mid height (shared Z for all three mids)
+    mid_z = float(0.5 * (lift_p[2] + hover_p[2]))
+
+    # Y coordinates:
+    y_lift  = float(lift_p[1])
+    y_hover = float(hover_p[1])
+    y_mid2  = 0.5 * (y_lift + y_hover)                 # halfway in Y (lift <-> hover)
+    y_mid1  = 0.5 * (y_lift + y_mid2)                  # halfway in Y (lift <-> mid2)
+    y_mid3  = 0.5 * (y_mid2  + y_hover)                # halfway in Y (mid2 <-> hover)
+
+    # -----------------------------
+    # mid1: +X,-Y (45 degrees), upright
+    # -----------------------------
+    mid1_p = np.array([0.5, y_mid1, 0.5], dtype=np.float32)
+    approach_xy = np.array([1.0, -1.0, 0.0], dtype=np.float32)
+    approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    target_closing = np.cross(z_up, approach_xy)
+    target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    tmp_mid1 = env.agent.build_grasp_pose(approach_xy, target_closing, mid1_p)
+    mid1_q = np.asarray(tmp_mid1.q, dtype=np.float32).reshape(-1)
+    mid1_q /= (np.linalg.norm(mid1_q) + 1e-9)
+
+    mid1_pose = Pose.create_from_pq(p=mid1_p, q=mid1_q)
+    res = planner.move_to_pose_with_CRRTConnect(mid1_pose, f=f, j=j)
+    if res == -1:
+        print("Failed to mid1 waypoint"); return res
+
+    # -----------------------------
+    # mid2: +X (straight out), upright  (your previous mid)
+    # -----------------------------
+    mid2_p = np.array([0.6, y_mid2, 0.5], dtype=np.float32)
+    approach_xy = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    target_closing = np.cross(z_up, approach_xy)
+    target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    tmp_mid2 = env.agent.build_grasp_pose(approach_xy, target_closing, mid2_p)
+    mid2_q = np.asarray(tmp_mid2.q, dtype=np.float32).reshape(-1)
+    mid2_q /= (np.linalg.norm(mid2_q) + 1e-9)
+
+    mid2_pose = Pose.create_from_pq(p=mid2_p, q=mid2_q)
+    res = planner.move_to_pose_with_CRRTConnect(mid2_pose)
+    if res == -1:
+        print("Failed to mid2 waypoint"); return res
+
+    # -----------------------------
+    # mid3: +X,+Y (45 degrees), upright
+    # -----------------------------
+    mid3_p = np.array([0.5, y_mid3, 0.5], dtype=np.float32)
+    approach_xy = np.array([1.0, 1.0, 0.0], dtype=np.float32)
+    approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    target_closing = np.cross(z_up, approach_xy)
+    target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    tmp_mid3 = env.agent.build_grasp_pose(approach_xy, target_closing, mid3_p)
+    mid3_q = np.asarray(tmp_mid3.q, dtype=np.float32).reshape(-1)
+    mid3_q /= (np.linalg.norm(mid3_q) + 1e-9)
+
+    mid3_pose = Pose.create_from_pq(p=mid3_p, q=mid3_q)
+    res = planner.move_to_pose_with_CRRTConnect(mid3_pose)
+    if res == -1:
+        print("Failed to mid3 waypoint"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Hover
+    # -------------------------------------------------------------------------- #
+    hover_pose = Pose.create_from_pq(p=hover_p, q=hover_q)
+    res = planner.move_to_pose_with_CRRTConnect(hover_pose)
+    if res == -1:
+        print("Failed to hover pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Lower & Release
+    # -------------------------------------------------------------------------- #
+    lower_p = p_goal_v.copy()        # (3,)
+    lower_p[2] += BOWL_Z   # slightly above the rack surface
+    lower_pose = Pose.create_from_pq(p=lower_p, q=hover_q)
+    res = planner.move_to_pose_with_RRTConnect(lower_pose)
+    if res == -1: 
+        print("Failed to lower pose")
+        return res
+
+    planner.open_gripper()
+   
+    return res
+
+import numpy as np
+
+def _get_downsampled_obstacle_pc(env, voxel: float = 0.03):
+    """
+    Grab the world-frame obstacle point cloud from the env, filter invalid points,
+    and voxel-downsample it.
+
+    Returns:
+        pc_ds (np.ndarray): (M, 3) float32 downsampled points.
+        voxel (float): The voxel size used (meters).
+    """
+    # 1) Fetch point cloud (torch or numpy) from the unwrapped env
+    pc = env.unwrapped.table_scene.obstacle_point_cloud
+    if hasattr(pc, "detach"):  # torch.Tensor
+        pc = pc.detach().cpu().numpy()
+    pc = np.asarray(pc, dtype=np.float32)
+
+    # 2) Normalize shape to (N, 3)
+    if pc.ndim == 3:
+        # handle (1, N, 3) or (B, N, 3); take first batch
+        pc = pc[0]
+    # handle potential (3, N)
+    if pc.shape[-1] != 3 and pc.shape[0] == 3:
+        pc = pc.T
+
+    assert pc.ndim == 2 and pc.shape[1] == 3, f"pc shape should be (N,3), got {pc.shape}"
+
+    # 3) Keep only finite points
+    mask = np.isfinite(pc).all(axis=1)
+    pc = pc[mask]
+
+    # 4) Voxel downsample (cheap grid keying)
+    keys = np.floor(pc / float(voxel)).astype(np.int32)
+    _, keep_idx = np.unique(keys, axis=0, return_index=True)
+    pc_ds = pc[keep_idx]
+
+    return pc_ds.astype(np.float32), float(voxel)
+
+def _quat_to_R_xyzw(q):
+    x,y,z,w = q
+    xx,yy,zz = x*x,y*y,z*z; xy,xz,yz = x*y,x*z,y*z; wx,wy,wz = w*x,w*y,w*z
+    return np.array([[1-2*(yy+zz), 2*(xy-wz),   2*(xz+wy)],
+                     [2*(xy+wz),   1-2*(xx+zz), 2*(yz-wx)],
+                     [2*(xz-wy),   2*(yz+wx),   1-2*(xx+yy)]], dtype=np.float64)
+
+def _R_ok(R):
+    return (np.all(np.isfinite(R))
+            and abs(np.linalg.det(R)-1.0) < 1e-3
+            and np.linalg.norm(R.T @ R - np.eye(3)) < 1e-3)
+
+def make_valid_xyzw_quat(q_in):
+    """Normalize, auto-fix order (wxyz→xyzw if needed), and nudge off π; returns xyzw float32."""
+    q = np.asarray(q_in, dtype=np.float64).reshape(4,)
+    n = np.linalg.norm(q)
+    if not np.isfinite(n) or n < 1e-12:
+        return np.array([0,0,0,1], dtype=np.float32)
+    q_xyzw = q / n
+    R = _quat_to_R_xyzw(q_xyzw)
+    if not _R_ok(R):  # try if input was wxyz
+        q_xyzw = np.array([q[1], q[2], q[3], q[0]], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+        R = _quat_to_R_xyzw(q_xyzw)
+        if not _R_ok(R):
+            q_xyzw = np.array([0,0,0,1], dtype=np.float64)
+    # de-singularize near 180° (w≈0)
+    if abs(q_xyzw[3]) < 1e-6:
+        q_xyzw = 0.999*q_xyzw + 0.001*np.array([0,0,0,1], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+    return q_xyzw.astype(np.float32)
+   
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv2.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bfe462ee227b83fb1357c97f89a00cdc218333c
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv2.py
@@ -0,0 +1,426 @@
+# Grasp cup task is working, but starting position is artificial
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+import time
+import random
+from transforms3d.euler import euler2quat
+
+import msx_envs.ms3.grasp_cup_v1
+from mani_skill.envs.tasks import GraspBowlEnv
+from motionplannerV2 import PandaArmMotionPlanningSolver
+from mani_skill.examples.motionplanning.panda.utils import compute_grasp_info_by_obb, get_actor_obb
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+
+def main():
+    env: GraspCupV1Env = gym.make(
+        "GraspCup-v1",
+        obs_mode="state_dict",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        reward_mode="dense",
+        robot_uids="panda"
+    )
+    
+    # Wrap the environment with RecordVideo
+    # env = gym.wrappers.RecordVideo(env, video_folder="./videos", episode_trigger=lambda x: True)
+
+    for seed in range(100):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: GraspBowlEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+    #print("Debug")
+
+    # Check collision shapes
+    # #print(f"Debug: Bowl collision shapes: {env.unwrapped.bowl.get_collision_shapes()}")
+    # #print(f"Debug: Rack collision shapes: {env.unwrapped.rack.get_collision_shapes()}")
+
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )   
+
+    pc, res = _get_downsampled_obstacle_pc(env, voxel=0.02)
+    planner.planner.update_point_cloud(points=pc, resolution=res)
+
+    env = env.unwrapped
+    FINGER_LENGTH = 0.025
+    BOWL_D = env.bowl_extents[0]
+    BOWL_Z = env.bowl_extents[2]
+    RACK_Z = env.rack_extents[2]
+    ENV_Z_OFFSET = BOWL_Z + RACK_Z
+
+    obb = get_actor_obb(env.bowl)
+
+    #--------------------------------------------------------------------------- #
+    # Constraint: limit EEF tilt angle from vertical (world +Z)
+    #--------------------------------------------------------------------------- #
+    # --- Upright constraint for CRRT (keep EEF +Z aligned with world +Z) ---
+    TILT_DEG = 0  # set e.g. 2.0 for ≤2° tilt allowance
+    COS_T = float(np.cos(np.deg2rad(TILT_DEG)))
+    EZ = np.array([0.0, 0.0, 1.0], dtype=np.float64)
+
+    _mplib = planner.planner                       # MPLib wrapper
+    _pin   = _mplib.pinocchio_model                # Pinocchio model wrapper
+    move_link_idx = _mplib.link_name_2_idx[_mplib.move_group]
+    move_group_joint_indices = _mplib.move_group_joint_indices  # indices for the move group (Ndof)
+
+    def _eef_z_from_q(q_movegroup: np.ndarray) -> np.ndarray:
+        """
+        Return EEF local +Z axis in world frame for the given move-group q.
+        Uses the same API your solver already uses: forward kinematics + get_link_pose.
+        """
+        # Expand to full robot configuration expected by Pinocchio
+        full_q = _mplib.pad_move_group_qpos(q_movegroup)  # shape (Nfull,)
+        # Forward kinematics at that config
+        _pin.compute_forward_kinematics(full_q)
+        new_pose = _pin.get_link_pose(move_link_idx)      # has .q quaternion
+        # Quaternion -> rotation matrix (ManiSkill util; returns torch tensor (3,3))
+        R = rotation_conversions.quaternion_to_matrix(torch.tensor(new_pose.q, dtype=torch.float32))
+        z_eef = (-R[:, 0]).cpu().numpy().astype(np.float64).reshape(-1)
+        z_eef /= (np.linalg.norm(z_eef) + 1e-12)
+        return z_eef
+
+    def f(self_, x, out):
+        """
+        Equality constraint: c(x) = z_eef(x)·EZ - cos(theta_max) = 0
+        For strictly upright: theta_max = 0 => cos = 1.
+        """
+        z_eef = _eef_z_from_q(x)
+        out[0] = float(z_eef.dot(EZ) - COS_T)
+
+
+    def j(self_, x, out):
+        """
+        Jacobian dc/dq. Using: d/dq_i (z·EZ) = ((ω_i × z) · EZ),
+        where ω_i is the angular part of the 6D geometric Jacobian at EEF.
+        """
+        # Get rotational Jacobian (3 x Ndof) for the move group at q=x
+        full_q = _mplib.pad_move_group_qpos(x)
+        J6 = _pin.compute_single_link_jacobian(full_q, move_link_idx)  # (6, Nfull)
+        rotJ = np.asarray(J6[3:, move_group_joint_indices], dtype=np.float64)  # angular rows
+
+        z_eef = _eef_z_from_q(x)
+        Ndof = rotJ.shape[1]
+
+        # MPLib provides `out` as (1, Ndof); handle both 1D/2D defensively
+        if out.ndim == 1:
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+        else:  # shape (1, Ndof)
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[0, i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+    #--------------------------------------------------------------------------- #
+    #--------------------------------------------------------------------------- #
+    # Save original TCP pose
+    tcp0 = env.agent.tcp.pose
+    orig_tcp_p = np.array(tcp0.p, dtype=np.float32).copy()
+    orig_tcp_q = make_valid_xyzw_quat(np.array(tcp0.q, dtype=np.float32).copy())
+
+    #--------------------------------------------------------------------------- #
+    # Compute horizontal grasp pose
+    #--------------------------------------------------------------------------- #
+    # --- Robustly get 3D positions from T matrices ---
+    T_tcp  = env.agent.tcp.pose.to_transformation_matrix()
+    T_bowl = env.bowl.pose.to_transformation_matrix()
+
+    # Handle batched vs non-batched; ensure numpy (3,)
+    tcp_p  = (T_tcp[0, :3, 3]  if T_tcp.ndim == 3  else T_tcp[:3, 3]).detach().cpu().numpy().astype(np.float32)
+    bowl_p = (T_bowl[0, :3, 3] if T_bowl.ndim == 3 else T_bowl[:3, 3]).detach().cpu().numpy().astype(np.float32)
+
+    half_h = 0.5 * env.bowl_extents[2]          # or from OBB: 0.5 * obb.extents[2]
+    center = bowl_p.copy()
+    center[2] += half_h*1.5                          # aim at mid-height
+
+    # --- Horizontal approach in XY ---
+    # --- Approach heading: from [-0.2, 0] toward bowl center (XY only) ---
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+    START_XY = np.array([-0.2, 0.0], dtype=np.float32)
+
+    # Vector in XY from fixed start point to the bowl center
+    dir_xy = center[:2] - START_XY
+    n = np.linalg.norm(dir_xy)
+
+    if n < 1e-6:
+        # Degenerate: bowl center ~ START_XY; default to +X
+        approaching = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    else:
+        approaching = np.array([dir_xy[0] / n, dir_xy[1] / n, 0.0], dtype=np.float32)
+
+    # If you ever want to approach outward instead of inward, flip:
+    # approaching = -approaching
+
+    # Horizontal closing axis orthogonal to 'approaching'
+    target_closing = np.cross(z_up, approaching)
+    m = np.linalg.norm(target_closing)
+    target_closing = (target_closing / m) if m > 1e-6 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+
+
+
+    grasp_info = compute_grasp_info_by_obb(
+        obb,
+        approaching=approaching,
+        target_closing=target_closing,
+        depth=FINGER_LENGTH
+    )
+    closing = grasp_info["closing"]
+ 
+    grasp_pose = env.agent.build_grasp_pose(approaching, closing, center)
+    grasp_q = make_valid_xyzw_quat(grasp_pose.q) # ensure valid
+
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    pre_back  = env.bowl_extents[0] * 0.50
+    target_z  = center[2] + 0.015
+    reach_pos = grasp_pose.p - approaching * pre_back
+    reach_pos[2] = target_z
+    reach_pose = Pose.create_from_pq(p=reach_pos, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose, screw_check = True)
+    if res == -1: 
+        print("Failed to reach pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Grasp
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+        
+    planner.close_gripper()
+
+    # -------------------------------------------------------------------------- #
+    # Lift
+    # -------------------------------------------------------------------------- #
+    lift_p  = grasp_pose.p + np.array([0.0, 0.0, 0.05], dtype=np.float32)
+    lift    = Pose.create_from_pq(p=lift_p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(lift)
+    if res == -1:
+        print("Failed to lift pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Compute HOVER target (position + upright orientation)
+    # -------------------------------------------------------------------------- #
+    CLEAR_Z = 0.15
+    p_goal, _ = env.get_goal_site_pose()
+    p_goal_v = np.asarray(p_goal, dtype=np.float32).squeeze()[:3]
+
+    hover_p = p_goal_v.copy()
+    hover_p[:2] += np.random.uniform(-0.03, 0.03, size=2).astype(np.float32)
+    hover_p[2]  += CLEAR_Z
+
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+    dir_xy = (hover_p - lift_p).astype(np.float32); dir_xy[2] = 0.0
+    n = np.linalg.norm(dir_xy)
+    approach_xy = (dir_xy / (n + 1e-9)) if n > 1e-9 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    target_closing = np.cross(z_up, approach_xy)
+    nc = np.linalg.norm(target_closing)
+    target_closing = (target_closing / (nc + 1e-9)) if nc > 1e-9 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+
+    tmp = env.agent.build_grasp_pose(approach_xy, target_closing, hover_p)
+    hover_q = np.asarray(tmp.q, dtype=np.float32).reshape(-1)
+    hover_q /= (np.linalg.norm(hover_q) + 1e-9)
+
+    # Optional sanity checks
+    assert np.all(np.isfinite(hover_p)); assert np.all(np.isfinite(hover_q))
+    assert abs(np.linalg.norm(hover_q) - 1.0) < 1e-3
+
+    # # -------------------------------------------------------------------------- #
+    # # Three mid waypoints (mid1, mid2, mid3), all upright
+    # # -------------------------------------------------------------------------- #
+    # # Common stuff
+    # z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+
+    # # "0.5 out" in X (half of the lift->hover delta in X)
+    # mid_x = float(lift_p[0] + 0.5 * (hover_p[0] - lift_p[0]))
+
+    # # Mid height (shared Z for all three mids)
+    # mid_z = float(0.5 * (lift_p[2] + hover_p[2]))
+
+    # # Y coordinates:
+    # y_lift  = float(lift_p[1])
+    # y_hover = float(hover_p[1])
+    # y_mid2  = 0.5 * (y_lift + y_hover)                 # halfway in Y (lift <-> hover)
+    # y_mid1  = 0.5 * (y_lift + y_mid2)                  # halfway in Y (lift <-> mid2)
+    # y_mid3  = 0.5 * (y_mid2  + y_hover)                # halfway in Y (mid2 <-> hover)
+
+    # # -----------------------------
+    # # mid1: +X,-Y (45 degrees), upright
+    # # -----------------------------
+    # mid1_p = np.array([0.5, y_mid1, 0.5], dtype=np.float32)
+    # approach_xy = np.array([1.0, -1.0, 0.0], dtype=np.float32)
+    # approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid1 = env.agent.build_grasp_pose(approach_xy, target_closing, mid1_p)
+    # mid1_q = np.asarray(tmp_mid1.q, dtype=np.float32).reshape(-1)
+    # mid1_q /= (np.linalg.norm(mid1_q) + 1e-9)
+
+    # mid1_pose = Pose.create_from_pq(p=mid1_p, q=mid1_q)
+    # # res = planner.move_to_pose_with_CRRTConnect(mid1_pose, f=f, j=j)
+    # if res == -1:
+    #     print("Failed to mid1 waypoint"); return res
+
+    # # -----------------------------
+    # # mid2: +X (straight out), upright  (your previous mid)
+    # # -----------------------------
+    # mid2_p = np.array([0.6, y_mid2, 0.4], dtype=np.float32)
+    # approach_xy = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid2 = env.agent.build_grasp_pose(approach_xy, target_closing, mid2_p)
+    # mid2_q = np.asarray(tmp_mid2.q, dtype=np.float32).reshape(-1)
+    # mid2_q /= (np.linalg.norm(mid2_q) + 1e-9)
+
+    # mid2_pose = Pose.create_from_pq(p=mid2_p, q=mid2_q)
+    # # res = planner.move_to_pose_with_CRRTConnect(mid2_pose, f=f, j=j)
+    # if res == -1:
+    #     print("Failed to mid2 waypoint"); return res
+
+    # # -----------------------------
+    # # mid3: +X,+Y (45 degrees), upright
+    # # -----------------------------
+    # mid3_p = np.array([0.5, y_mid3, 0.5], dtype=np.float32)
+    # approach_xy = np.array([1.0, 1.0, 0.0], dtype=np.float32)
+    # approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid3 = env.agent.build_grasp_pose(approach_xy, target_closing, mid3_p)
+    # mid3_q = np.asarray(tmp_mid3.q, dtype=np.float32).reshape(-1)
+    # mid3_q /= (np.linalg.norm(mid3_q) + 1e-9)
+
+    # mid3_pose = Pose.create_from_pq(p=mid3_p, q=mid3_q)
+    # # res = planner.move_to_pose_with_CRRTConnect(mid3_pose)
+    # if res == -1:
+    #     print("Failed to mid3 waypoint"); return res
+    
+    # -----------------------------
+    # Intermediate return to original pose
+    # -----------------------------
+    orig_tcp_pose = Pose.create_from_pq(p=orig_tcp_p, q=orig_tcp_q)
+    res = planner.move_to_pose_with_CRRTConnect(orig_tcp_pose, f=f, j=j)
+    if res == -1:
+        print("Failed to return to original TCP pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Hover
+    # -------------------------------------------------------------------------- #
+    hover_pose = Pose.create_from_pq(p=hover_p, q=hover_q)
+    res = planner.move_to_pose_with_CRRTConnect(hover_pose, f=f, j=j)
+    if res == -1:
+        print("Failed to hover pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Lower & Release
+    # -------------------------------------------------------------------------- #
+    lower_p = p_goal_v.copy()        # (3,)
+    lower_p[2] += BOWL_Z   # slightly above the rack surface
+    lower_pose = Pose.create_from_pq(p=lower_p, q=hover_q)
+    res = planner.move_to_pose_with_RRTConnect(lower_pose)
+    if res == -1: 
+        print("Failed to lower pose")
+        return res
+
+    planner.open_gripper()
+   
+    return res
+
+import numpy as np
+
+def _get_downsampled_obstacle_pc(env, voxel: float = 0.03):
+    """
+    Grab the world-frame obstacle point cloud from the env, filter invalid points,
+    and voxel-downsample it.
+
+    Returns:
+        pc_ds (np.ndarray): (M, 3) float32 downsampled points.
+        voxel (float): The voxel size used (meters).
+    """
+    # 1) Fetch point cloud (torch or numpy) from the unwrapped env
+    pc = env.unwrapped.table_scene.obstacle_point_cloud
+    if hasattr(pc, "detach"):  # torch.Tensor
+        pc = pc.detach().cpu().numpy()
+    pc = np.asarray(pc, dtype=np.float32)
+
+    # 2) Normalize shape to (N, 3)
+    if pc.ndim == 3:
+        # handle (1, N, 3) or (B, N, 3); take first batch
+        pc = pc[0]
+    # handle potential (3, N)
+    if pc.shape[-1] != 3 and pc.shape[0] == 3:
+        pc = pc.T
+
+    assert pc.ndim == 2 and pc.shape[1] == 3, f"pc shape should be (N,3), got {pc.shape}"
+
+    # 3) Keep only finite points
+    mask = np.isfinite(pc).all(axis=1)
+    pc = pc[mask]
+
+    # 4) Voxel downsample (cheap grid keying)
+    keys = np.floor(pc / float(voxel)).astype(np.int32)
+    _, keep_idx = np.unique(keys, axis=0, return_index=True)
+    pc_ds = pc[keep_idx]
+
+    return pc_ds.astype(np.float32), float(voxel)
+
+def _quat_to_R_xyzw(q):
+    x,y,z,w = q
+    xx,yy,zz = x*x,y*y,z*z; xy,xz,yz = x*y,x*z,y*z; wx,wy,wz = w*x,w*y,w*z
+    return np.array([[1-2*(yy+zz), 2*(xy-wz),   2*(xz+wy)],
+                     [2*(xy+wz),   1-2*(xx+zz), 2*(yz-wx)],
+                     [2*(xz-wy),   2*(yz+wx),   1-2*(xx+yy)]], dtype=np.float64)
+
+def _R_ok(R):
+    return (np.all(np.isfinite(R))
+            and abs(np.linalg.det(R)-1.0) < 1e-3
+            and np.linalg.norm(R.T @ R - np.eye(3)) < 1e-3)
+
+def make_valid_xyzw_quat(q_in):
+    """Normalize, auto-fix order (wxyz→xyzw if needed), and nudge off π; returns xyzw float32."""
+    q = np.asarray(q_in, dtype=np.float64).reshape(4,)
+    n = np.linalg.norm(q)
+    if not np.isfinite(n) or n < 1e-12:
+        return np.array([0,0,0,1], dtype=np.float32)
+    q_xyzw = q / n
+    R = _quat_to_R_xyzw(q_xyzw)
+    if not _R_ok(R):  # try if input was wxyz
+        q_xyzw = np.array([q[1], q[2], q[3], q[0]], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+        R = _quat_to_R_xyzw(q_xyzw)
+        if not _R_ok(R):
+            q_xyzw = np.array([0,0,0,1], dtype=np.float64)
+    # de-singularize near 180° (w≈0)
+    if abs(q_xyzw[3]) < 1e-6:
+        q_xyzw = 0.999*q_xyzw + 0.001*np.array([0,0,0,1], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+    return q_xyzw.astype(np.float32)
+   
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv3.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce4bb75b1dace54467ff40c445c2e4cf0404e173
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/grasp_cup_testv3.py
@@ -0,0 +1,420 @@
+# intermediate + constrained movements, but movement is twisty and weird
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+import time
+import random
+from transforms3d.euler import euler2quat
+
+import msx_envs.ms3.grasp_cup_v1
+from mani_skill.envs.tasks import GraspBowlEnv
+from msx_envs.solutions.motionplannerV2 import PandaArmMotionPlanningSolver
+from mani_skill.examples.motionplanning.panda.utils import compute_grasp_info_by_obb, get_actor_obb
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+
+def main():
+    env: GraspCupV1Env = gym.make(
+        "GraspCup-v1",
+        obs_mode="state_dict",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        reward_mode="dense",
+        robot_uids="panda"
+    )
+    
+    # Wrap the environment with RecordVideo
+    env = gym.wrappers.RecordVideo(env, video_folder="./videos", episode_trigger=lambda x: True)
+
+    for seed in range(100):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: GraspBowlEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+    #print("Debug")
+
+    # Check collision shapes
+    # #print(f"Debug: Bowl collision shapes: {env.unwrapped.bowl.get_collision_shapes()}")
+    # #print(f"Debug: Rack collision shapes: {env.unwrapped.rack.get_collision_shapes()}")
+
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )   
+
+    pc, res = _get_downsampled_obstacle_pc(env, voxel=0.02)
+    planner.planner.update_point_cloud(pc, resolution=res)
+
+    env = env.unwrapped
+    FINGER_LENGTH = 0.025
+    BOWL_D = env.bowl_extents[0]
+    BOWL_Z = env.bowl_extents[2]
+    RACK_Z = env.rack_extents[2]
+    ENV_Z_OFFSET = BOWL_Z + RACK_Z
+
+    obb = get_actor_obb(env.bowl)
+
+    #--------------------------------------------------------------------------- #
+    # Constraint: limit EEF tilt angle from vertical (world +Z) - keep the cup upright
+    #--------------------------------------------------------------------------- #
+    # --- Upright constraint for CRRT (keep EEF +Z aligned with world +Z) ---
+    TILT_DEG = 0 
+    COS_T = float(np.cos(np.deg2rad(TILT_DEG)))
+    EZ = np.array([0.0, 0.0, 1.0], dtype=np.float64)
+
+    _mplib = planner.planner                       # MPLib wrapper
+    _pin   = _mplib.pinocchio_model                # Pinocchio model wrapper
+    move_link_idx = _mplib.link_name_2_idx[_mplib.move_group]
+    move_group_joint_indices = _mplib.move_group_joint_indices  # indices for the move group (Ndof)
+
+    def _eef_z_from_q(q_movegroup: np.ndarray) -> np.ndarray:
+        """
+        Return EEF local +Z axis in world frame for the given move-group q.
+        Uses the same API your solver already uses: forward kinematics + get_link_pose.
+        """
+        # Expand to full robot configuration expected by Pinocchio
+        full_q = _mplib.pad_move_group_qpos(q_movegroup)  # shape (Nfull,)
+        # Forward kinematics at that config
+        _pin.compute_forward_kinematics(full_q)
+        new_pose = _pin.get_link_pose(move_link_idx)      # has .q quaternion
+        # Quaternion -> rotation matrix (ManiSkill util; returns torch tensor (3,3))
+        R = rotation_conversions.quaternion_to_matrix(torch.tensor(new_pose.q, dtype=torch.float32))
+        z_eef = (-R[:, 0]).cpu().numpy().astype(np.float64).reshape(-1)
+        z_eef /= (np.linalg.norm(z_eef) + 1e-12)
+        return z_eef
+
+    def f(self_, x, out):
+        """
+        Equality constraint: c(x) = z_eef(x)·EZ - cos(theta_max) = 0
+        For strictly upright: theta_max = 0 => cos = 1.
+        """
+        z_eef = _eef_z_from_q(x)
+        out[0] = float(z_eef.dot(EZ) - COS_T)
+
+
+    def j(self_, x, out):
+        """
+        Jacobian dc/dq. Using: d/dq_i (z·EZ) = ((ω_i × z) · EZ),
+        where ω_i is the angular part of the 6D geometric Jacobian at EEF.
+        """
+        # Get rotational Jacobian (3 x Ndof) for the move group at q=x
+        full_q = _mplib.pad_move_group_qpos(x)
+        J6 = _pin.compute_single_link_jacobian(full_q, move_link_idx)  # (6, Nfull)
+        rotJ = np.asarray(J6[3:, move_group_joint_indices], dtype=np.float64)  # angular rows
+
+        z_eef = _eef_z_from_q(x)
+        Ndof = rotJ.shape[1]
+
+        # MPLib provides `out` as (1, Ndof); handle both 1D/2D defensively
+        if out.ndim == 1:
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+        else:  # shape (1, Ndof)
+            for i in range(Ndof):
+                omega_i = rotJ[:, i]
+                out[0, i] = float(np.dot(np.cross(omega_i, z_eef), EZ))
+    #--------------------------------------------------------------------------- #
+    #--------------------------------------------------------------------------- #
+
+    #--------------------------------------------------------------------------- #
+    # Compute horizontal grasp pose
+    #--------------------------------------------------------------------------- #
+    # --- Robustly get 3D positions from T matrices ---
+    T_tcp  = env.agent.tcp.pose.to_transformation_matrix()
+    T_bowl = env.bowl.pose.to_transformation_matrix()
+
+    # Handle batched vs non-batched; ensure numpy (3,)
+    tcp_p  = (T_tcp[0, :3, 3]  if T_tcp.ndim == 3  else T_tcp[:3, 3]).detach().cpu().numpy().astype(np.float32)
+    bowl_p = (T_bowl[0, :3, 3] if T_bowl.ndim == 3 else T_bowl[:3, 3]).detach().cpu().numpy().astype(np.float32)
+
+    half_h = 0.5 * env.bowl_extents[2]          # or from OBB: 0.5 * obb.extents[2]
+    center = bowl_p.copy()
+    center[2] += half_h*1.5                          # aim at 3/4 height
+
+    # --- Horizontal approach in XY ---
+    vec_xy = center - tcp_p            # (3,)
+    vec_xy[2] = 0.0                    # zero Z safely now
+    norm = np.linalg.norm(vec_xy)
+    vec_xy[2] = 0.0
+    approaching = vec_xy / (np.linalg.norm(vec_xy) + 1e-9)
+
+    # Closing axis: z_up × approaching  (also horizontal)
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+    target_closing = np.cross(z_up, approaching)
+    n = np.linalg.norm(target_closing)
+    target_closing = (target_closing / n) if n > 1e-6 else np.array([1.0, 0.0, 0.0], dtype=np.float32)
+
+
+    grasp_info = compute_grasp_info_by_obb(
+        obb,
+        approaching=approaching,
+        target_closing=target_closing,
+        depth=FINGER_LENGTH
+    )
+    closing = grasp_info["closing"]
+ 
+    grasp_pose = env.agent.build_grasp_pose(approaching, closing, center)
+    grasp_q = make_valid_xyzw_quat(grasp_pose.q) # ensure valid
+
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    pre_back  = env.bowl_extents[0] * 0.60
+    target_z  = center[2] + 0.015
+    reach_pos = grasp_pose.p - approaching * pre_back
+    reach_pos[2] = target_z
+    reach_pose = Pose.create_from_pq(p=reach_pos, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to reach pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Grasp
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose, screw_check=True)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+        
+    planner.close_gripper()
+
+    # -------------------------------------------------------------------------- #
+    # Lift
+    # -------------------------------------------------------------------------- #
+    lift_p  = grasp_pose.p + np.array([0.0, 0.0, 0.05], dtype=np.float32)
+    lift    = Pose.create_from_pq(p=lift_p, q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(lift)
+    if res == -1:
+        print("Failed to lift pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Compute HOVER target (position + upright orientation)
+    # -------------------------------------------------------------------------- #
+    CLEAR_Z = 0.20
+    ANGLE_DEG_CW = 20.0  # set your clockwise angle in degrees
+
+    p_goal, _ = env.get_goal_site_pose()
+    p_goal_v = np.asarray(p_goal, dtype=np.float32).squeeze()[:3]
+
+    hover_p = p_goal_v.copy()
+    hover_p[:2] += np.random.uniform(-0.03, 0.03, size=2).astype(np.float32)
+    hover_p[2]  += CLEAR_Z
+
+    z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+
+    # --- Approach from world origin -> goal in XY, then rotate CW by ANGLE_DEG_CW ---
+    v_xy = hover_p[:2].astype(np.float32)                    # vector from (0,0) to goal (x,y)
+    norm = np.linalg.norm(v_xy)
+    if norm < 1e-9:
+        base_xy = np.array([1.0, 0.0], dtype=np.float32)     # fallback if goal is at origin
+    else:
+        base_xy = v_xy / norm
+
+    theta = np.deg2rad(ANGLE_DEG_CW)
+    # Clockwise rotation about +Z is a negative angle in right-handed coords
+    c, s = np.cos(-theta), np.sin(-theta)
+    rot_xy = np.array([[c, -s],
+                    [s,  c]], dtype=np.float32)
+    approach_xy_2d = rot_xy @ base_xy
+    approach_xy = np.array([approach_xy_2d[0], approach_xy_2d[1], 0.0], dtype=np.float32)
+
+    # --- Keep tool upright: closing axis = z_up × approach ---
+    target_closing = np.cross(z_up, approach_xy)
+    nc = np.linalg.norm(target_closing)
+    if nc < 1e-9:
+        target_closing = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    else:
+        target_closing = target_closing / nc
+
+    # --- Build pose + normalized quaternion ---
+    tmp = env.agent.build_grasp_pose(approach_xy, target_closing, hover_p)
+    hover_q = np.asarray(tmp.q, dtype=np.float32).reshape(-1)
+    hover_q /= (np.linalg.norm(hover_q) + 1e-9)
+
+    # # -------------------------------------------------------------------------- #
+    # # Three mid waypoints (mid1, mid2, mid3), all upright
+    # # -------------------------------------------------------------------------- #
+    # # Common stuff
+    # z_up = np.array([0.0, 0.0, 1.0], dtype=np.float32)
+
+    # # "0.5 out" in X (half of the lift->hover delta in X)
+    # mid_x = float(lift_p[0] + 0.5 * (hover_p[0] - lift_p[0]))
+
+    # # Mid height (shared Z for all three mids)
+    # mid_z = float(0.5 * (lift_p[2] + hover_p[2]))
+
+    # # Y coordinates:
+    # y_lift  = float(lift_p[1])
+    # y_hover = float(hover_p[1])
+    # y_mid2  = 0.5 * (y_lift + y_hover)                 # halfway in Y (lift <-> hover)
+    # y_mid1  = 0.5 * (y_lift + y_mid2)                  # halfway in Y (lift <-> mid2)
+    # y_mid3  = 0.5 * (y_mid2  + y_hover)                # halfway in Y (mid2 <-> hover)
+
+    # # -----------------------------
+    # # mid1: +X,-Y (45 degrees), upright
+    # # -----------------------------
+    # mid1_p = np.array([0.5, y_mid1, 0.5], dtype=np.float32)
+    # approach_xy = np.array([1.0, -1.0, 0.0], dtype=np.float32)
+    # approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid1 = env.agent.build_grasp_pose(approach_xy, target_closing, mid1_p)
+    # mid1_q = np.asarray(tmp_mid1.q, dtype=np.float32).reshape(-1)
+    # mid1_q /= (np.linalg.norm(mid1_q) + 1e-9)
+
+    # mid1_pose = Pose.create_from_pq(p=mid1_p, q=mid1_q)
+    # res = planner.move_to_pose_with_CRRTConnect(mid1_pose, f=f, j=j)
+    # if res == -1:
+    #     print("Failed to mid1 waypoint"); return res
+
+    # # -----------------------------
+    # # mid2: +X (straight out), upright  (your previous mid)
+    # # -----------------------------
+    # mid2_p = np.array([0.6, y_mid2, 0.5], dtype=np.float32)
+    # approach_xy = np.array([1.0, 0.0, 0.0], dtype=np.float32)
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid2 = env.agent.build_grasp_pose(approach_xy, target_closing, mid2_p)
+    # mid2_q = np.asarray(tmp_mid2.q, dtype=np.float32).reshape(-1)
+    # mid2_q /= (np.linalg.norm(mid2_q) + 1e-9)
+
+    # mid2_pose = Pose.create_from_pq(p=mid2_p, q=mid2_q)
+    # res = planner.move_to_pose_with_CRRTConnect(mid2_pose)
+    # if res == -1:
+    #     print("Failed to mid2 waypoint"); return res
+
+    # # -----------------------------
+    # # mid3: +X,+Y (45 degrees), upright
+    # # -----------------------------
+    # mid3_p = np.array([0.5, y_mid3, 0.5], dtype=np.float32)
+    # approach_xy = np.array([1.0, 1.0, 0.0], dtype=np.float32)
+    # approach_xy[:2] /= (np.linalg.norm(approach_xy[:2]) + 1e-9)
+
+    # target_closing = np.cross(z_up, approach_xy)
+    # target_closing /= (np.linalg.norm(target_closing) + 1e-9)
+
+    # tmp_mid3 = env.agent.build_grasp_pose(approach_xy, target_closing, mid3_p)
+    # mid3_q = np.asarray(tmp_mid3.q, dtype=np.float32).reshape(-1)
+    # mid3_q /= (np.linalg.norm(mid3_q) + 1e-9)
+
+    # mid3_pose = Pose.create_from_pq(p=mid3_p, q=mid3_q)
+    # res = planner.move_to_pose_with_CRRTConnect(mid3_pose)
+    # if res == -1:
+    #     print("Failed to mid3 waypoint"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Hover
+    # -------------------------------------------------------------------------- #
+    hover_pose = Pose.create_from_pq(p=hover_p, q=hover_q)
+    res = planner.move_to_pose_with_CRRTConnect(hover_pose, screw_check=True)
+    if res == -1:
+        print("Failed to hover pose"); return res
+
+    # -------------------------------------------------------------------------- #
+    # Lower & Release
+    # -------------------------------------------------------------------------- #
+    lower_p = p_goal_v.copy()        # (3,)
+    lower_p[2] += BOWL_Z   # slightly above the rack surface
+    lower_pose = Pose.create_from_pq(p=lower_p, q=hover_q)
+    res = planner.move_to_pose_with_RRTConnect(lower_pose, screw_check=True)
+    if res == -1: 
+        print("Failed to lower pose")
+        return res
+
+    planner.open_gripper()
+   
+    return res
+
+import numpy as np
+
+def _get_downsampled_obstacle_pc(env, voxel: float = 0.03):
+    """
+    Grab the world-frame obstacle point cloud from the env, filter invalid points,
+    and voxel-downsample it.
+
+    Returns:
+        pc_ds (np.ndarray): (M, 3) float32 downsampled points.
+        voxel (float): The voxel size used (meters).
+    """
+    # 1) Fetch point cloud (torch or numpy) from the unwrapped env
+    pc = env.unwrapped.table_scene.obstacle_point_cloud
+    if hasattr(pc, "detach"):  # torch.Tensor
+        pc = pc.detach().cpu().numpy()
+    pc = np.asarray(pc, dtype=np.float32)
+
+    # 2) Normalize shape to (N, 3)
+    if pc.ndim == 3:
+        # handle (1, N, 3) or (B, N, 3); take first batch
+        pc = pc[0]
+    # handle potential (3, N)
+    if pc.shape[-1] != 3 and pc.shape[0] == 3:
+        pc = pc.T
+
+    assert pc.ndim == 2 and pc.shape[1] == 3, f"pc shape should be (N,3), got {pc.shape}"
+
+    # 3) Keep only finite points
+    mask = np.isfinite(pc).all(axis=1)
+    pc = pc[mask]
+
+    # 4) Voxel downsample (cheap grid keying)
+    keys = np.floor(pc / float(voxel)).astype(np.int32)
+    _, keep_idx = np.unique(keys, axis=0, return_index=True)
+    pc_ds = pc[keep_idx]
+
+    return pc_ds.astype(np.float32), float(voxel)
+
+def _quat_to_R_xyzw(q):
+    x,y,z,w = q
+    xx,yy,zz = x*x,y*y,z*z; xy,xz,yz = x*y,x*z,y*z; wx,wy,wz = w*x,w*y,w*z
+    return np.array([[1-2*(yy+zz), 2*(xy-wz),   2*(xz+wy)],
+                     [2*(xy+wz),   1-2*(xx+zz), 2*(yz-wx)],
+                     [2*(xz-wy),   2*(yz+wx),   1-2*(xx+yy)]], dtype=np.float64)
+
+def _R_ok(R):
+    return (np.all(np.isfinite(R))
+            and abs(np.linalg.det(R)-1.0) < 1e-3
+            and np.linalg.norm(R.T @ R - np.eye(3)) < 1e-3)
+
+def make_valid_xyzw_quat(q_in):
+    """Normalize, auto-fix order (wxyz→xyzw if needed), and nudge off π; returns xyzw float32."""
+    q = np.asarray(q_in, dtype=np.float64).reshape(4,)
+    n = np.linalg.norm(q)
+    if not np.isfinite(n) or n < 1e-12:
+        return np.array([0,0,0,1], dtype=np.float32)
+    q_xyzw = q / n
+    R = _quat_to_R_xyzw(q_xyzw)
+    if not _R_ok(R):  # try if input was wxyz
+        q_xyzw = np.array([q[1], q[2], q[3], q[0]], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+        R = _quat_to_R_xyzw(q_xyzw)
+        if not _R_ok(R):
+            q_xyzw = np.array([0,0,0,1], dtype=np.float64)
+    # de-singularize near 180° (w≈0)
+    if abs(q_xyzw[3]) < 1e-6:
+        q_xyzw = 0.999*q_xyzw + 0.001*np.array([0,0,0,1], dtype=np.float64)
+        q_xyzw /= (np.linalg.norm(q_xyzw) + 1e-12)
+    return q_xyzw.astype(np.float32)
+   
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplanner.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplanner.py
new file mode 100644
index 0000000000000000000000000000000000000000..d16fabe8cb23930f6cbc31b88516cb09804bc021
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplanner.py
@@ -0,0 +1,339 @@
+import mplib
+import numpy as np
+import sapien
+import trimesh
+from functools import partial
+from typing import Callable, Optional
+import torch
+
+from mani_skill.agents.base_agent import BaseAgent
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.envs.scene import ManiSkillScene
+from mani_skill.utils.structs.pose import to_sapien_pose
+from mani_skill.utils.geometry import rotation_conversions
+import sapien.physx as physx
+OPEN = 1
+CLOSED = -1
+
+
+class PandaArmMotionPlanningSolver:
+    def __init__(
+        self,
+        env: BaseEnv,
+        debug: bool = False,
+        vis: bool = True,
+        base_pose: sapien.Pose = None,  # TODO mplib doesn't support robot base being anywhere but 0
+        visualize_target_grasp_pose: bool = True,
+        print_env_info: bool = True,
+        joint_vel_limits=0.9,
+        joint_acc_limits=0.9,
+    ):
+        self.env = env
+        self.base_env: BaseEnv = env.unwrapped
+        self.env_agent: BaseAgent = self.base_env.agent
+        self.robot = self.env_agent.robot
+        self.tcp = self.env_agent.tcp
+        self.joint_vel_limits = joint_vel_limits
+        self.joint_acc_limits = joint_acc_limits
+
+        self.base_pose = to_sapien_pose(base_pose)
+
+        self.planner = self.setup_planner()
+        self.control_mode = self.base_env.control_mode
+
+        self.debug = debug
+        self.vis = vis
+        self.print_env_info = print_env_info
+        self.visualize_target_grasp_pose = visualize_target_grasp_pose
+        self.gripper_state = OPEN
+        self.grasp_pose_visual = None
+        if self.vis and self.visualize_target_grasp_pose:
+            if "grasp_pose_visual" not in self.base_env.scene.actors:
+                self.grasp_pose_visual = build_panda_gripper_grasp_pose_visual(
+                    self.base_env.scene
+                )
+            else:
+                self.grasp_pose_visual = self.base_env.scene.actors["grasp_pose_visual"]
+            self.grasp_pose_visual.set_pose(self.base_env.agent.tcp.pose)
+        self.elapsed_steps = 0
+
+        self.use_point_cloud = False
+        self.collision_pts_changed = False
+        self.all_collision_pts = None
+
+    def render_wait(self):
+        if not self.vis or not self.debug:
+            return
+        print("Press [c] to continue")
+        viewer = self.base_env.render_human()
+        while True:
+            if viewer.window.key_down("c"):
+                break
+            self.base_env.render_human()
+
+    def setup_planner(self):
+        link_names = [link.get_name() for link in self.robot.get_links()]
+        joint_names = [joint.get_name() for joint in self.robot.get_active_joints()]
+        planner = mplib.Planner(
+            urdf=self.env_agent.urdf_path,
+            srdf=self.env_agent.urdf_path.replace(".urdf", ".srdf"),
+            user_link_names=link_names,
+            user_joint_names=joint_names,
+            move_group="panda_hand_tcp",
+            joint_vel_limits=np.ones(7) * self.joint_vel_limits,
+            joint_acc_limits=np.ones(7) * self.joint_acc_limits,
+        )
+        # if mplib version 0.2.1
+        b_pose = mplib.Pose(self.base_pose.p, self.base_pose.q)
+        planner.set_base_pose(b_pose)
+
+        # elif mplib version 0.1.1
+        # planner.set_base_pose(np.hstack([self.base_pose.p, self.base_pose.q]))
+        return planner
+
+    def follow_path(self, result, refine_steps: int = 0):
+        n_step = result["position"].shape[0]
+        for i in range(n_step + refine_steps):
+            qpos = result["position"][min(i, n_step - 1)]
+            if self.control_mode == "pd_joint_pos_vel":
+                qvel = result["velocity"][min(i, n_step - 1)]
+                action = np.hstack([qpos, qvel, self.gripper_state])
+            else:
+                action = np.hstack([qpos, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def move_to_pose_with_RRTConnect(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0
+    ):
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+        result = self.planner.plan_pose(
+            # np.concatenate([pose.p, pose.q]),
+            mplib.Pose(pose.p, pose.q),
+            self.robot.get_qpos().cpu().numpy()[0],
+            time_step=self.base_env.control_timestep,
+            # use_point_cloud=self.use_point_cloud,
+            wrt_world=True,
+        )
+        if result["status"] != "Success":
+            print(result["status"])
+            self.render_wait()
+            return -1
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def move_to_pose_with_screw(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0, trials: int = 10
+    ):
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+        
+        base_q = pose.q  # save the original quaternion
+        noise_level = 1e-2  # small noise scale; adjust as needed
+        result = None
+
+        for attempt in range(trials):
+            # For all trials after the first, add a small random perturbation
+            if attempt > 0:
+                noise = np.random.normal(scale=noise_level, size=base_q.shape)
+                noisy_q = base_q + noise
+                noisy_q = noisy_q / np.linalg.norm(noisy_q)  # re-normalize to ensure a valid quaternion
+            else:
+                noisy_q = base_q
+
+            # Create a new pose for this trial using the potentially noisy quaternion
+            trial_pose = sapien.Pose(p=pose.p, q=noisy_q)
+            result = self.planner.plan_screw(
+                mplib.Pose(trial_pose.p, trial_pose.q),
+                self.robot.get_qpos().cpu().numpy()[0],
+                time_step=self.base_env.control_timestep,
+            )
+            if result["status"] == "Success":
+                break
+
+        if result is None or result["status"] != "Success":
+            print(result["status"] if result is not None else "No result")
+            self.render_wait()
+            return -1
+
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def make_f(self, f):
+        if f is not None:
+            return partial(f, self)
+
+    def make_j(self, j):
+        if j is not None:
+            return partial(j, self)
+
+    def get_eef_x(self):
+        move_link_idx = self.planner.link_name_2_idx[self.planner.move_group]
+        move_joint_idx = self.planner.move_group_joint_indices
+        self.planner.pinocchio_model.compute_forward_kinematics(self.planner.robot.get_qpos())
+        new_pose = self.planner.pinocchio_model.get_link_pose(move_link_idx)
+        eef_rot = rotation_conversions.quaternion_to_matrix(torch.tensor(new_pose.q))
+        eef_x = eef_rot[:, 0].cpu().numpy().astype(np.float32).reshape(-1)
+        # eef_y = eef_rot[:, 1].cpu().numpy().astype(np.float32).reshape(-1)
+        # eef_z = eef_rot[:, 2].cpu().numpy().astype(np.float32).reshape(-1)
+        return eef_x
+
+    def move_to_pose_with_CRRTConnect(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0,
+        f: Optional[Callable] = None, j: Optional[Callable] = None,
+    ):
+
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+        # print(self.get_eef_z())
+        # breakpoint()
+        # print("control time step")
+        # print(self.base_env.control_timestep)
+        result = self.planner.plan_pose(
+            # np.concatenate([pose.p, pose.q]),
+            mplib.Pose(pose.p, pose.q),
+            self.robot.get_qpos().cpu().numpy()[0],
+            time_step=self.base_env.control_timestep,
+            # use_point_cloud=self.use_point_cloud,
+            wrt_world=True,
+            constraint_function=self.make_f(f),
+            constraint_jacobian=self.make_j(j),
+            constraint_tolerance= 1e-2,
+        )
+        if result["status"] != "Success":
+            print(result["status"])
+            self.render_wait()
+            return -1
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def open_gripper(self):
+        self.gripper_state = OPEN
+        qpos = self.robot.get_qpos()[0, :-2].cpu().numpy()
+        for i in range(6):
+            if self.control_mode == "pd_joint_pos":
+                action = np.hstack([qpos, self.gripper_state])
+            else:
+                action = np.hstack([qpos, qpos * 0, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def close_gripper(self, t=6, gripper_state = CLOSED):
+        self.gripper_state = gripper_state
+        qpos = self.robot.get_qpos()[0, :-2].cpu().numpy()
+        for i in range(t):
+            if self.control_mode == "pd_joint_pos":
+                action = np.hstack([qpos, self.gripper_state])
+            else:
+                action = np.hstack([qpos, qpos * 0, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def add_box_collision(self, extents: np.ndarray, pose: sapien.Pose):
+        self.use_point_cloud = True
+        box = trimesh.creation.box(extents, transform=pose.to_transformation_matrix())
+        pts, _ = trimesh.sample.sample_surface(box, 256)
+        if self.all_collision_pts is None:
+            self.all_collision_pts = pts
+        else:
+            self.all_collision_pts = np.vstack([self.all_collision_pts, pts])
+        self.planner.update_point_cloud(self.all_collision_pts)
+
+    def add_collision_pts(self, pts: np.ndarray):
+        if self.all_collision_pts is None:
+            self.all_collision_pts = pts
+        else:
+            self.all_collision_pts = np.vstack([self.all_collision_pts, pts])
+        self.planner.update_point_cloud(self.all_collision_pts)
+
+    def clear_collisions(self):
+        self.all_collision_pts = None
+        self.use_point_cloud = False
+
+    def close(self):
+        pass
+
+from transforms3d import quaternions
+
+
+def build_panda_gripper_grasp_pose_visual(scene: ManiSkillScene):
+    builder = scene.create_actor_builder()
+    grasp_pose_visual_width = 0.01
+    grasp_width = 0.05
+
+    builder.add_sphere_visual(
+        pose=sapien.Pose(p=[0, 0, 0.0]),
+        radius=grasp_pose_visual_width,
+        material=sapien.render.RenderMaterial(base_color=[0.3, 0.4, 0.8, 0.7])
+    )
+
+    builder.add_box_visual(
+        pose=sapien.Pose(p=[0, 0, -0.08]),
+        half_size=[grasp_pose_visual_width, grasp_pose_visual_width, 0.02],
+        material=sapien.render.RenderMaterial(base_color=[0, 1, 0, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(p=[0, 0, -0.05]),
+        half_size=[grasp_pose_visual_width, grasp_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[0, 1, 0, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(
+            p=[
+                0.03 - grasp_pose_visual_width * 3,
+                grasp_width + grasp_pose_visual_width,
+                0.03 - 0.05,
+            ],
+            q=quaternions.axangle2quat(np.array([0, 1, 0]), theta=np.pi / 2),
+        ),
+        half_size=[0.04, grasp_pose_visual_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[0, 0, 1, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(
+            p=[
+                0.03 - grasp_pose_visual_width * 3,
+                -grasp_width - grasp_pose_visual_width,
+                0.03 - 0.05,
+            ],
+            q=quaternions.axangle2quat(np.array([0, 1, 0]), theta=np.pi / 2),
+        ),
+        half_size=[0.04, grasp_pose_visual_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[1, 0, 0, 0.7]),
+    )
+    grasp_pose_visual = builder.build_kinematic(name="grasp_pose_visual")
+    return grasp_pose_visual
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplannerV2.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplannerV2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d57abf9ac8f3abe11730ef3f166b9394d8ad5a11
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/motionplannerV2.py
@@ -0,0 +1,388 @@
+# Same as motionplanner.py but with changed CRRTConnect and screw check in RRTConnect
+import mplib
+import numpy as np
+import sapien
+import trimesh
+from functools import partial
+from typing import Callable, Optional
+import torch
+
+from mani_skill.agents.base_agent import BaseAgent
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.envs.scene import ManiSkillScene
+from mani_skill.utils.structs.pose import to_sapien_pose
+from mani_skill.utils.geometry import rotation_conversions
+import sapien.physx as physx
+OPEN = 1
+CLOSED = -1
+
+
+class PandaArmMotionPlanningSolver:
+    def __init__(
+        self,
+        env: BaseEnv,
+        debug: bool = False,
+        vis: bool = True,
+        base_pose: sapien.Pose = None,  # TODO mplib doesn't support robot base being anywhere but 0
+        visualize_target_grasp_pose: bool = True,
+        print_env_info: bool = True,
+        joint_vel_limits=0.9,
+        joint_acc_limits=0.9,
+    ):
+        self.env = env
+        self.base_env: BaseEnv = env.unwrapped
+        self.env_agent: BaseAgent = self.base_env.agent
+        self.robot = self.env_agent.robot
+        self.tcp = self.env_agent.tcp
+        self.joint_vel_limits = joint_vel_limits
+        self.joint_acc_limits = joint_acc_limits
+
+        self.base_pose = to_sapien_pose(base_pose)
+
+        self.planner = self.setup_planner()
+        self.control_mode = self.base_env.control_mode
+
+        self.debug = debug
+        self.vis = vis
+        self.print_env_info = print_env_info
+        self.visualize_target_grasp_pose = visualize_target_grasp_pose
+        self.gripper_state = OPEN
+        self.grasp_pose_visual = None
+        if self.vis and self.visualize_target_grasp_pose:
+            if "grasp_pose_visual" not in self.base_env.scene.actors:
+                self.grasp_pose_visual = build_panda_gripper_grasp_pose_visual(
+                    self.base_env.scene
+                )
+            else:
+                self.grasp_pose_visual = self.base_env.scene.actors["grasp_pose_visual"]
+            self.grasp_pose_visual.set_pose(self.base_env.agent.tcp.pose)
+        self.elapsed_steps = 0
+
+        self.use_point_cloud = True
+        self.collision_pts_changed = False
+        self.all_collision_pts = None
+
+    def render_wait(self):
+        if not self.vis or not self.debug:
+            return
+        print("Press [c] to continue")
+        viewer = self.base_env.render_human()
+        while True:
+            if viewer.window.key_down("c"):
+                break
+            self.base_env.render_human()
+
+    def setup_planner(self):
+        link_names = [link.get_name() for link in self.robot.get_links()]
+        joint_names = [joint.get_name() for joint in self.robot.get_active_joints()]
+        planner = mplib.Planner(
+            urdf=self.env_agent.urdf_path,
+            srdf=self.env_agent.urdf_path.replace(".urdf", ".srdf"),
+            user_link_names=link_names,
+            user_joint_names=joint_names,
+            move_group="panda_hand_tcp",
+            joint_vel_limits=np.ones(7) * self.joint_vel_limits,
+            joint_acc_limits=np.ones(7) * self.joint_acc_limits,
+        )
+        # if mplib version 0.2.1
+        b_pose = mplib.Pose(self.base_pose.p, self.base_pose.q)
+        planner.set_base_pose(b_pose)
+
+        # elif mplib version 0.1.1
+        # planner.set_base_pose(np.hstack([self.base_pose.p, self.base_pose.q]))
+        return planner
+
+    def follow_path(self, result, refine_steps: int = 0):
+        n_step = result["position"].shape[0]
+        for i in range(n_step + refine_steps):
+            qpos = result["position"][min(i, n_step - 1)]
+            if self.control_mode == "pd_joint_pos_vel":
+                qvel = result["velocity"][min(i, n_step - 1)]
+                action = np.hstack([qpos, qvel, self.gripper_state])
+            else:
+                action = np.hstack([qpos, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def move_to_pose_with_RRTConnect(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0, screw_check = False,
+    ):
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+
+        result = self.planner.plan_pose(
+            # np.concatenate([pose.p, pose.q]),
+            mplib.Pose(pose.p, pose.q),
+            self.robot.get_qpos().cpu().numpy()[0],
+            time_step=self.base_env.control_timestep,
+            # use_point_cloud=self.use_point_cloud,
+            wrt_world=True,
+        )
+
+        if result["status"] != "Success":
+            print(result["status"])
+            self.render_wait()
+            return -1
+
+        # ---- Optional anti-screw guard ----
+        if screw_check:
+            JOINT_IDX = 4                 # joint 5 (0-based)
+            MAX_DELTA = np.deg2rad(235)
+
+            traj = result.get("position", None)
+            if traj is not None and np.size(traj) > 0:
+                traj = np.asarray(traj, dtype=float)        # (N, DoF)
+                j5 = traj[:, JOINT_IDX]  #isolate joint 5 trajectory
+                j5_unw = np.unwrap(j5)                       # 1-D unwrap
+                delta_end = float(j5_unw[-1] - j5_unw[0])    # scalar
+
+                if abs(delta_end) > MAX_DELTA + 1e-6:
+                    print(
+                        f"Rejected plan: joint {JOINT_IDX} start→end change "
+                        f"{np.rad2deg(abs(delta_end)):.1f}° (> 235°)."
+                    )
+                    self.render_wait()
+                    return -1
+            else:
+                print("Warning: no trajectory positions in planner result; skipping joint-rotation check.")
+        
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def move_to_pose_with_screw(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0, trials: int = 10
+    ):
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+        
+        base_q = pose.q  # save the original quaternion
+        noise_level = 1e-2  # small noise scale; adjust as needed
+        result = None
+
+        for attempt in range(trials):
+            # For all trials after the first, add a small random perturbation
+            if attempt > 0:
+                noise = np.random.normal(scale=noise_level, size=base_q.shape)
+                noisy_q = base_q + noise
+                noisy_q = noisy_q / np.linalg.norm(noisy_q)  # re-normalize to ensure a valid quaternion
+            else:
+                noisy_q = base_q
+
+            # Create a new pose for this trial using the potentially noisy quaternion
+            trial_pose = sapien.Pose(p=pose.p, q=noisy_q)
+            result = self.planner.plan_screw(
+                mplib.Pose(trial_pose.p, trial_pose.q),
+                self.robot.get_qpos().cpu().numpy()[0],
+                time_step=self.base_env.control_timestep,
+            )
+            if result["status"] == "Success":
+                break
+
+        if result is None or result["status"] != "Success":
+            print(result["status"] if result is not None else "No result")
+            self.render_wait()
+            return -1
+
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def make_f(self, f):
+        if f is not None:
+            return partial(f, self)
+
+    def make_j(self, j):
+        if j is not None:
+            return partial(j, self)
+
+    def get_eef_x(self):
+        move_link_idx = self.planner.link_name_2_idx[self.planner.move_group]
+        move_joint_idx = self.planner.move_group_joint_indices
+        self.planner.pinocchio_model.compute_forward_kinematics(self.planner.robot.get_qpos())
+        new_pose = self.planner.pinocchio_model.get_link_pose(move_link_idx)
+        eef_rot = rotation_conversions.quaternion_to_matrix(torch.tensor(new_pose.q))
+        eef_x = eef_rot[:, 0].cpu().numpy().astype(np.float32).reshape(-1)
+        # eef_y = eef_rot[:, 1].cpu().numpy().astype(np.float32).reshape(-1)
+        # eef_z = eef_rot[:, 2].cpu().numpy().astype(np.float32).reshape(-1)
+        return eef_x
+
+    def move_to_pose_with_CRRTConnect(
+        self, pose: sapien.Pose, dry_run: bool = False, refine_steps: int = 0,
+        f: Optional[Callable] = None, j: Optional[Callable] = None, screw_check = False,
+    ):
+
+        pose = to_sapien_pose(pose)
+        if self.grasp_pose_visual is not None:
+            self.grasp_pose_visual.set_pose(pose)
+        pose = sapien.Pose(p=pose.p, q=pose.q)
+        # print(self.get_eef_z())
+        # breakpoint()
+        # print("control time step")
+        # print(self.base_env.control_timestep)
+        result = self.planner.plan_pose(
+            # np.concatenate([pose.p, pose.q]),
+            mplib.Pose(pose.p, pose.q),
+            self.robot.get_qpos().cpu().numpy()[0],
+            time_step=self.base_env.control_timestep,
+            # use_point_cloud=self.use_point_cloud,
+            wrt_world=True,
+            constraint_function=self.make_f(f),
+            constraint_jacobian=self.make_j(j),
+            constraint_tolerance= 5e-2,
+        )
+        if result["status"] != "Success":
+            print(result["status"])
+            self.render_wait()
+            return -1
+        
+        # ---- Optional anti-screw guard ----
+        if screw_check:
+            JOINT_IDX = 4                 # joint 5 (0-based)
+            MAX_DELTA = np.deg2rad(235)
+
+            traj = result.get("position", None)
+            if traj is not None and np.size(traj) > 0:
+                traj = np.asarray(traj, dtype=float)        # (N, DoF)
+                j5 = traj[:, JOINT_IDX]  #isolate joint 5 trajectory
+                j5_unw = np.unwrap(j5)                       # 1-D unwrap
+                delta_end = float(j5_unw[-1] - j5_unw[0])    # scalar
+
+                if abs(delta_end) > MAX_DELTA + 1e-6:
+                    print(
+                        f"Rejected plan: joint {JOINT_IDX} start→end change "
+                        f"{np.rad2deg(abs(delta_end)):.1f}° (> 235°)."
+                    )
+                    self.render_wait()
+                    return -1
+            else:
+                print("Warning: no trajectory positions in planner result; skipping joint-rotation check.")
+
+        self.render_wait()
+        if dry_run:
+            return result
+        return self.follow_path(result, refine_steps=refine_steps)
+
+    def open_gripper(self):
+        self.gripper_state = OPEN
+        qpos = self.robot.get_qpos()[0, :-2].cpu().numpy()
+        for i in range(6):
+            if self.control_mode == "pd_joint_pos":
+                action = np.hstack([qpos, self.gripper_state])
+            else:
+                action = np.hstack([qpos, qpos * 0, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def close_gripper(self, t=6, gripper_state = CLOSED):
+        self.gripper_state = gripper_state
+        qpos = self.robot.get_qpos()[0, :-2].cpu().numpy()
+        for i in range(t):
+            if self.control_mode == "pd_joint_pos":
+                action = np.hstack([qpos, self.gripper_state])
+            else:
+                action = np.hstack([qpos, qpos * 0, self.gripper_state])
+            obs, reward, terminated, truncated, info = self.env.step(action)
+            self.elapsed_steps += 1
+            if self.print_env_info:
+                print(
+                    f"[{self.elapsed_steps:3}] Env Output: reward={reward} info={info}"
+                )
+            if self.vis:
+                self.base_env.render_human()
+        return obs, reward, terminated, truncated, info
+
+    def add_box_collision(self, extents: np.ndarray, pose: sapien.Pose):
+        self.use_point_cloud = True
+        box = trimesh.creation.box(extents, transform=pose.to_transformation_matrix())
+        pts, _ = trimesh.sample.sample_surface(box, 256)
+        if self.all_collision_pts is None:
+            self.all_collision_pts = pts
+        else:
+            self.all_collision_pts = np.vstack([self.all_collision_pts, pts])
+        self.planner.update_point_cloud(self.all_collision_pts)
+
+    def add_collision_pts(self, pts: np.ndarray):
+        if self.all_collision_pts is None:
+            self.all_collision_pts = pts
+        else:
+            self.all_collision_pts = np.vstack([self.all_collision_pts, pts])
+        self.planner.update_point_cloud(self.all_collision_pts)
+
+    def clear_collisions(self):
+        self.all_collision_pts = None
+        self.use_point_cloud = False
+
+    def close(self):
+        pass
+
+from transforms3d import quaternions
+
+
+def build_panda_gripper_grasp_pose_visual(scene: ManiSkillScene):
+    builder = scene.create_actor_builder()
+    grasp_pose_visual_width = 0.01
+    grasp_width = 0.05
+
+    builder.add_sphere_visual(
+        pose=sapien.Pose(p=[0, 0, 0.0]),
+        radius=grasp_pose_visual_width,
+        material=sapien.render.RenderMaterial(base_color=[0.3, 0.4, 0.8, 0.7])
+    )
+
+    builder.add_box_visual(
+        pose=sapien.Pose(p=[0, 0, -0.08]),
+        half_size=[grasp_pose_visual_width, grasp_pose_visual_width, 0.02],
+        material=sapien.render.RenderMaterial(base_color=[0, 1, 0, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(p=[0, 0, -0.05]),
+        half_size=[grasp_pose_visual_width, grasp_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[0, 1, 0, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(
+            p=[
+                0.03 - grasp_pose_visual_width * 3,
+                grasp_width + grasp_pose_visual_width,
+                0.03 - 0.05,
+            ],
+            q=quaternions.axangle2quat(np.array([0, 1, 0]), theta=np.pi / 2),
+        ),
+        half_size=[0.04, grasp_pose_visual_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[0, 0, 1, 0.7]),
+    )
+    builder.add_box_visual(
+        pose=sapien.Pose(
+            p=[
+                0.03 - grasp_pose_visual_width * 3,
+                -grasp_width - grasp_pose_visual_width,
+                0.03 - 0.05,
+            ],
+            q=quaternions.axangle2quat(np.array([0, 1, 0]), theta=np.pi / 2),
+        ),
+        half_size=[0.04, grasp_pose_visual_width, grasp_pose_visual_width],
+        material=sapien.render.RenderMaterial(base_color=[1, 0, 0, 0.7]),
+    )
+    grasp_pose_visual = builder.build_kinematic(name="grasp_pose_visual")
+    return grasp_pose_visual
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/pick_box.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/pick_box.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f2d1a450f4cca8df227a85dc198ab9e7f4efdec
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/pick_box.py
@@ -0,0 +1,246 @@
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+
+import msx_envs.ms3.grasp_box_v0
+from mani_skill.envs.tasks import PickCubeEnv
+from mani_skill.examples.motionplanning.panda.motionplanner import \
+    PandaArmMotionPlanningSolver
+from mani_skill.examples.motionplanning.panda.utils import (
+    compute_grasp_info_by_obb, get_actor_obb)
+
+import sys
+sys.path.append('/home/kyber/charles/project/grasp_box')
+# from perception_function import Perception
+from scipy.spatial.transform import Rotation
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.geometry import rotation_conversions
+
+
+def main():
+    env: GraspBoxEnv = gym.make(
+        "GraspBox-v0",
+        obs_mode="state_dict",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        reward_mode="dense",
+        robot_uids="panda",
+        distractor_count = 32,
+        rand_level=2,
+    )
+    
+    # Wrap the environment with RecordVideo
+    # env = gym.wrappers.RecordVideo(env, video_folder="./videos", episode_trigger=lambda x: True)
+
+    for seed in range(100):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: PickCubeEnv, seed=None, debug=False, vis=False):
+    try:
+        env.reset(seed=seed)
+    except Exception as e:
+        print(f"Failed to reset environment: {e}")
+        return -1
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+
+    # import time
+    # time.sleep(3.0)  # wait for a while to let the env settle down
+    
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )   
+
+    FINGER_LENGTH = 0.025
+    env = env.unwrapped
+
+    # Create grasp pose
+    grasp_pose = create_aligned_grasp_pose(env)
+
+    # forcing to be aligned with box. Sorry I know it's a shitty way to do it :))
+    euler = rotation_conversions.quaternion_to_euler(torch.tensor(grasp_pose.q).reshape(1, -1))
+    euler[:, 2] = np.pi
+    grasp_q = np.array(rotation_conversions.euler_to_quaternion(euler)).reshape(-1)
+    grasp_pose = sapien.Pose(grasp_pose.p, grasp_q)
+
+    goal_pose = [0.3,-0.6,0.6]
+    mid_pose = [0.3,-0.3,0.7] # 1st level of shelf
+
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    reach_pose = grasp_pose * sapien.Pose([0, 0, -0.15])
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to reach pose")
+        return res
+
+    # -------------------------------------------------------------------------- #
+    # Grasp
+    # -------------------------------------------------------------------------- #
+    # import time
+    # time.sleep(4.0)  # wait for a while to let the env settle down
+    res = planner.move_to_pose_with_RRTConnect(grasp_pose)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+    planner.close_gripper()
+
+
+
+    # -------------------------------------------------------------------------- #
+    # Pick up slightly
+    # -------------------------------------------------------------------------- #
+    reach_pose = Pose.create_from_pq(p=grasp_pose.p+[0, 0, 0.3], q=grasp_pose.q)
+    # reach_pose = grasp_pose +  sapien.Pose([0, 0, -0.5])
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to grasp pose")
+        return res
+   
+
+    # # # -------------------------------------------------------------------------- #
+    # # # Move to mid pose
+    # # # -------------------------------------------------------------------------- #
+    # goal_pose = sapien.Pose(goal_pos, grasp_pose.q)
+    rot = Rotation.from_euler('zyx', [np.pi/2, np.pi, -np.pi/2])
+    quaternion = rot.as_quat()
+    mid_pose = sapien.Pose(mid_pose, quaternion)
+    res = planner.move_to_pose_with_RRTConnect(mid_pose)
+    if res == -1: 
+        print("Failed to goal pose")
+        return res
+
+    goal_pose = sapien.Pose(goal_pose, quaternion)
+    res = planner.move_to_pose_with_screw(goal_pose)
+    if res == -1: 
+        print("Failed to goal pose")
+        return res
+
+    planner.open_gripper()
+
+    res = planner.move_to_pose_with_RRTConnect(mid_pose)
+    if res == -1: 
+        print("Failed to goal pose")
+        return res
+
+    planner.close()
+    return res
+
+def create_aligned_grasp_pose(env):
+    """
+    Creates a grasp pose to the top of the box, aligned with the box's orientation.
+    
+    Args:
+        env: The environment containing the box and gripper
+    
+    Returns:
+        grasp_pose: A Sapien Pose object for the aligned grasp
+    """
+    obb = get_actor_obb(env.box)
+    # Get box rotation to determine its orientation
+    box_pose = env.box.pose
+    box_transform = box_pose.to_transformation_matrix()
+    if hasattr(box_transform, 'cpu'):
+        box_transform = box_transform.cpu().numpy()
+    if box_transform.ndim == 3:
+        box_transform = box_transform[0]
+    box_rotation = box_transform[:3, :3]
+    
+    # Extract the box's yaw angle (rotation about Z-axis)
+    box_x_axis = box_rotation[:, 0]
+    box_yaw = np.arctan2(box_x_axis[1], box_x_axis[0])
+    
+    # Get the top center of the box for grasp position
+    grasp_world = obb.centroid.copy()
+    grasp_world[2] += obb.extents[2] / 2  # Offset to top surface
+    grasp_world[2] -= 0.045  # Offset because the box falls down on reset
+    
+    # Define approach direction (from above)
+    approaching = np.array([0, 0, -1])
+    
+    # Get gripper's closing direction (Y-axis of TCP)
+    target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy()
+    
+    # Build the grasp pose rotation matrix
+    z_axis = -approaching  # Z-axis points up (opposite of approach)
+    y_axis = target_closing / np.linalg.norm(target_closing)  # Closing direction
+    x_axis = np.cross(y_axis, z_axis)  # Complete right-handed frame
+    x_axis = x_axis / np.linalg.norm(x_axis)
+    
+    rotation_matrix = np.column_stack([x_axis, y_axis, z_axis])
+    
+    # DON'T flip - test rotation first
+    # rotation_matrix[:, 0] = -rotation_matrix[:, 0]  # Negate x-axis
+    # rotation_matrix[:, 1] = -rotation_matrix[:, 1]  # Negate y-axis
+
+    # TEST ROTATIONS - uncomment one at a time to see the effect
+
+
+    # Test rotation around local X-axis (roll - gripper wrist rotation)
+    test_angle = np.radians(30)  # 45 degrees for testing
+    rot_local_x = Rotation.from_euler('x', -box_yaw, degrees=False).as_matrix()
+    rotation_matrix = rotation_matrix @ rot_local_x
+    # print("Rotating around local X-axis (wrist roll)")
+
+    # Convert to quaternion
+    quat = Rotation.from_matrix(rotation_matrix).as_quat()  # [x, y, z, w]
+
+    # Create grasp pose
+    grasp_pose = sapien.Pose(p=grasp_world, q=quat)
+    return grasp_pose
+
+    
+
+    # Version without box alignment:-----------------------------------------------
+    # approaching = np.array([0, 0, -1])  # panda approaches along -Z axis
+
+    # # get transformation matrix of the tcp pose, is default batched and on torch
+    # target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy()
+
+
+    # # Get the top center of the box
+    # grasp_world = obb.centroid.copy()
+    # grasp_world[2] += obb.extents[2] / 2 - 0.05 # top surface of box
+
+    # # Define approach direction (from above)
+    # approaching = np.array([0, 0, -1])  # Approach downward along -Z
+
+    # # Get gripper's closing direction (Y-axis of TCP)
+    # target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy()
+
+    # # Build the grasp pose rotation matrix
+    # # Z-axis: opposite of approach direction (pointing up)
+    # z_axis = -approaching
+    # # Y-axis: closing direction (normalized)
+    # y_axis = target_closing / np.linalg.norm(target_closing)
+    # # X-axis: completes right-handed frame
+    # x_axis = np.cross(y_axis, z_axis)
+    # x_axis = x_axis / np.linalg.norm(x_axis)  # Normalize
+
+    # rotation_matrix = np.column_stack([x_axis, y_axis, z_axis])
+    #  # Flip 180 degrees around local z-axis by negating x and y axes
+    # rotation_matrix[:, 1] = -rotation_matrix[:, 1]  # Negate x-axis
+    # rotation_matrix[:, 2] = -rotation_matrix[:, 2]  # Negate y-axis
+
+    # # Convert to quaternion
+    # from scipy.spatial.transform import Rotation
+    # quat = Rotation.from_matrix(rotation_matrix).as_quat()  # [x, y, z, w]
+
+    # # Create grasp pose
+    # grasp_pose = sapien.Pose(p=grasp_world, q=quat)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/simple_reach.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/simple_reach.py
new file mode 100644
index 0000000000000000000000000000000000000000..55bac3ac885af0a2868cdbd5fdffd123c4bc3d52
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/simple_reach.py
@@ -0,0 +1,152 @@
+import gymnasium as gym
+import numpy as np
+import sapien
+import torch
+import time
+import random
+from transforms3d.euler import euler2quat
+
+# from mani_skill.envs.tasks import GraspBowlEnv
+from mani_skill.examples.motionplanning.panda.utils import compute_grasp_info_by_obb, get_actor_obb
+from mani_skill.utils.geometry import rotation_conversions
+from mani_skill.utils.structs.pose import Pose
+
+from msx_envs.solutions.motionplanner import PandaArmMotionPlanningSolver
+# from msx_envs.empty_random_env import EmptyRandomEnv
+from msx_envs import empty_random_env
+
+
+def main():
+    env: EmptyRandomEnv = gym.make(
+        "EmptyRandom-v0",
+        obs_mode="state_dict",
+        control_mode="pd_joint_pos",
+        render_mode="rgb_array",
+        reward_mode="dense",
+        robot_uids="panda"
+    )
+
+    for seed in range(30):
+        res = solve(env, seed=seed, debug=False, vis=True)
+        #print(res[-1])
+    env.close()
+
+def solve(env: empty_random_env.EmptyRandomEnv, seed=None, debug=False, vis=False):
+    env.reset(seed=seed)
+    assert env.unwrapped.control_mode in [
+        "pd_joint_pos",
+        "pd_joint_pos_vel",
+    ], env.unwrapped.control_mode
+    #print("Debug")
+
+    # Check collision shapes
+    # #print(f"Debug: Bowl collision shapes: {env.unwrapped.bowl.get_collision_shapes()}")
+    # #print(f"Debug: Rack collision shapes: {env.unwrapped.rack.get_collision_shapes()}")
+
+    planner = PandaArmMotionPlanningSolver(
+        env,
+        debug=debug,
+        vis=vis,
+        base_pose=env.unwrapped.agent.robot.pose,
+        visualize_target_grasp_pose=vis,
+        print_env_info=False,
+        joint_vel_limits=0.75,
+        joint_acc_limits=0.75,
+    )
+    
+    env = env.unwrapped
+    # -------------------------------------------------------------------------- #
+    # Reach
+    # -------------------------------------------------------------------------- #
+    reach_pose = env.goal_pose
+    # reach_pose = Pose.create_from_pq(p=grasp_pose.p + [random.uniform(-0.05, 0.05), BOWL_D*0.28+random.uniform(-0.05, 0.05), BOWL_Z * random.uniform(4.0, 5.0)], q=grasp_q)
+    res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    if res == -1: 
+        print("Failed to reach pose")
+    return res
+
+    # FINGER_LENGTH = 0.025
+    # BOWL_D = env.bowl_extents[0]
+    # BOWL_Z = env.bowl_extents[2]
+    # RACK_Z = env.rack_extents[2]
+    # ENV_Z_OFFSET = BOWL_Z + RACK_Z + FINGER_LENGTH
+
+    # obb = get_actor_obb(env.bowl)
+ 
+    # approaching = np.array([0, 0, -1], dtype=np.float32)
+    # target_closing = env.agent.tcp.pose.to_transformation_matrix()[0, :3, 1].cpu().numpy().astype(np.float32)
+
+    # grasp_info = compute_grasp_info_by_obb(
+    #     obb,
+    #     approaching=approaching,
+    #     target_closing=target_closing,
+    #     depth=FINGER_LENGTH
+    # )
+    # closing, center = grasp_info["closing"], grasp_info["center"]
+ 
+    # grasp_pose = env.agent.build_grasp_pose(approaching, closing, env.bowl.pose.p)
+    # euler = rotation_conversions.quaternion_to_euler(torch.tensor(grasp_pose.q).reshape(1, -1))
+    # euler[:, 0] = 0
+    # grasp_q = np.array(rotation_conversions.euler_to_quaternion(euler)).reshape(-1)
+
+    # # -------------------------------------------------------------------------- #
+    # # Reach
+    # # -------------------------------------------------------------------------- #
+    # reach_pose = Pose.create_from_pq(p=grasp_pose.p + [random.uniform(-0.05, 0.05), BOWL_D*0.28+random.uniform(-0.05, 0.05), BOWL_Z * random.uniform(4.0, 5.0)], q=grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    # if res == -1: 
+    #     print("Failed to reach pose")
+    #     return res
+
+    # # -------------------------------------------------------------------------- #
+    # # Grasp
+    # # -------------------------------------------------------------------------- #
+    # reach_pose = Pose.create_from_pq(p=grasp_pose.p + [0, BOWL_D*0.28, BOWL_Z*0.0], q=grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(reach_pose)
+    # if res == -1: 
+    #     print("Failed to grasp pose")
+    #     return res
+        
+    # planner.close_gripper()
+
+    # # -------------------------------------------------------------------------- #
+    # # Lift
+    # # -------------------------------------------------------------------------- #
+    # lift_pose = Pose.create_from_pq(p=grasp_pose.p + [random.uniform(-0.1, 0.1), BOWL_D*0.28 + random.uniform(-0.1, 0.1) , BOWL_Z* random.uniform(2.0, 5.0)], q=grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(lift_pose)
+    # if res == -1: 
+    #     print("Failed to lift pose")
+    #     return res
+
+    # # lift_pose = Pose.create_from_pq(p=grasp_pose.p + [0, BOWL_D*0.28, 1.5*ENV_Z_OFFSET], q=grasp_q)
+    # # res = planner.move_to_pose_with_RRTConnect(lift_pose)
+    # # if res == -1: 
+    # #     print("Failed to lift pose")
+    # #     return res
+
+    # # # -------------------------------------------------------------------------- #
+    # # # Hover over goalsite (rack pose)
+    # # # -------------------------------------------------------------------------- #
+    # p, q = env.get_goal_site_pose()
+    # pose = Pose.create_from_pq(p=p, q=q)
+    # hover_pose = sapien.Pose(pose.sp.p + [random.uniform(-0.1, 0.1), BOWL_Z+random.uniform(-0.1, 0.1), BOWL_Z*random.uniform(2.0, 4.0)], grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(hover_pose)
+    # if res == -1: 
+    #     print("Failed to hover pose")
+    #     return res
+
+    # # -------------------------------------------------------------------------- #
+    # # Lower & Release
+    # # -------------------------------------------------------------------------- #
+    # lower_pose = sapien.Pose(pose.sp.p + [0, BOWL_Z, BOWL_Z/10], grasp_q)
+    # res = planner.move_to_pose_with_RRTConnect(lower_pose)
+    # if res == -1: 
+    #     print("Failed to lower pose")
+    #     return res
+
+    # planner.open_gripper()
+   
+    # return res
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/utils.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..cc65734a821fe14f251ec9a959a72decac5d4c69
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/solutions/utils.py
@@ -0,0 +1,90 @@
+import numpy as np
+import sapien
+import sapien.physx as physx
+import sapien.render
+import trimesh
+from transforms3d import quaternions
+from mani_skill.utils.structs import Actor
+from mani_skill.utils import common
+from mani_skill.utils.geometry.trimesh_utils import get_component_mesh
+
+
+def get_actor_obb(actor: Actor, to_world_frame=True, vis=False):
+    mesh = get_component_mesh(
+        actor._objs[0].find_component_by_type(physx.PhysxRigidDynamicComponent),
+        to_world_frame=to_world_frame,
+    )
+    assert mesh is not None, "can not get actor mesh for {}".format(actor)
+
+    obb: trimesh.primitives.Box = mesh.bounding_box_oriented
+
+    if vis:
+        obb.visual.vertex_colors = (255, 0, 0, 10)
+        trimesh.Scene([mesh, obb]).show()
+
+    return obb
+
+
+def compute_grasp_info_by_obb(
+    obb: trimesh.primitives.Box,
+    approaching=(0, 0, -1),
+    target_closing=None,
+    depth=0.0,
+    ortho=True,
+):
+    """Compute grasp info given an oriented bounding box.
+    The grasp info includes axes to define grasp frame, namely approaching, closing, orthogonal directions and center.
+
+    Args:
+        obb: oriented bounding box to grasp
+        approaching: direction to approach the object
+        target_closing: target closing direction, used to select one of multiple solutions
+        depth: displacement from hand to tcp along the approaching vector. Usually finger length.
+        ortho: whether to orthogonalize closing  w.r.t. approaching.
+    """
+    # NOTE(jigu): DO NOT USE `x.extents`, which is inconsistent with `x.primitive.transform`!
+    extents = np.array(obb.primitive.extents)
+    T = np.array(obb.primitive.transform)
+
+    # Assume normalized
+    approaching = np.array(approaching)
+
+    # Find the axis closest to approaching vector
+    angles = approaching @ T[:3, :3]  # [3]
+    inds0 = np.argsort(np.abs(angles))
+    ind0 = inds0[-1]
+
+    # Find the shorter axis as closing vector
+    inds1 = np.argsort(extents[inds0[0:-1]])
+    ind1 = inds0[0:-1][inds1[0]]
+    ind2 = inds0[0:-1][inds1[1]]
+
+    # If sizes are close, choose the one closest to the target closing
+    if target_closing is not None and 0.99 < (extents[ind1] / extents[ind2]) < 1.01:
+        vec1 = T[:3, ind1]
+        vec2 = T[:3, ind2]
+        if np.abs(target_closing @ vec1) < np.abs(target_closing @ vec2):
+            ind1 = inds0[0:-1][inds1[1]]
+            ind2 = inds0[0:-1][inds1[0]]
+    closing = T[:3, ind1]
+
+    # Flip if far from target
+    if target_closing is not None and target_closing @ closing < 0:
+        closing = -closing
+
+    # Reorder extents
+    extents = extents[[ind0, ind1, ind2]]
+
+    # Find the origin on the surface
+    center = T[:3, 3].copy()
+    half_size = extents[0] * 0.5
+    center = center + approaching * (-half_size + min(depth, half_size))
+
+    if ortho:
+        closing = closing - (approaching @ closing) * approaching
+        closing = common.np_normalize_vector(closing)
+
+    grasp_info = dict(
+        approaching=approaching, closing=closing, center=center, extents=extents
+    )
+    return grasp_info
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/__init__.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/clutter_scene_builder.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/clutter_scene_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..52a4ed464c868e1ecee0a98b5c44fd2975114625
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/clutter_scene_builder.py
@@ -0,0 +1,10 @@
+from typing import Any, Dict, Union
+
+import numpy as np
+import sapien
+import torch
+import torch.random
+from transforms3d.euler import euler2quat
+
+from mani_skill.utils.scene_builder.table import TableSceneBuilder
+
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/enviroment_utils.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/enviroment_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/object_placement.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/object_placement.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ebb82ca9f4fdcc27c48dc07a4df9dc7e7477c39
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/msx_envs/utils/object_placement.py
@@ -0,0 +1,91 @@
+"""Extracted object placement algorithm."""
+
+from dataclasses import dataclass
+from typing import List, Tuple
+
+import numpy as np
+import sapien.core as sapien
+
+
+@dataclass(frozen=True)
+class ObjectBoundingBox:
+    min: tuple[float, float, float]
+    max: tuple[float, float, float]
+
+
+@dataclass(frozen=True)
+class PlacementBounds:
+    low: tuple[float, float]
+    high: tuple[float, float]
+    size: tuple[int, ...]
+
+    def random_in(self, rng: np.random.RandomState):
+        return rng.uniform(self.low, self.high, size=self.size)
+
+def is_overlapping(bb1_min, bb1_max, bb2_min, bb2_max, pos1, pos2, padding=0.05, check_z=False):
+    # Translate bounding boxes based on positions
+    bb1_min_trans = np.add(bb1_min, pos1) - padding
+    bb1_max_trans = np.add(bb1_max, pos1) + padding
+    bb2_min_trans = np.add(bb2_min, pos2) - padding
+    bb2_max_trans = np.add(bb2_max, pos2) + padding
+    
+    # Check for overlap in each dimension
+    overlap_x = not (bb1_max_trans[0] < bb2_min_trans[0] or bb2_max_trans[0] < bb1_min_trans[0])
+    overlap_y = not (bb1_max_trans[1] < bb2_min_trans[1] or bb2_max_trans[1] < bb1_min_trans[1])
+    overlap_z = False
+    if check_z:
+        overlap_z = not (bb1_max_trans[2] < bb2_min_trans[2] or bb2_max_trans[2] < bb1_min_trans[2])
+        return overlap_x and overlap_y and overlap_z
+    
+    return overlap_x and overlap_y
+
+
+def find_valid_position_2d(placement_bounds: PlacementBounds, actor: sapien.Actor, actor_local_bbox: ObjectBoundingBox, placed_objects: List[Tuple[sapien.Actor, ObjectBoundingBox]], rng: np.random.RandomState, retries: int, minimum_aabb_separation: float=0.005) -> tuple[float, float]:
+    """Bounding box is for cached collision check.
+    
+    This uses the AABB relative to the actor.
+    """
+    attempt = 0
+
+    # get half width to keep the actor from the edge...
+    half_obj_xy_dim = 0.5 * (actor_local_bbox.max - actor_local_bbox.min)[:2]
+    assert (
+        np.linalg.norm(half_obj_xy_dim) 
+        < np.linalg.norm(np.subtract(placement_bounds.high, placement_bounds.low))
+    ), "Surface is much smaller than object"
+    low_bounds = placement_bounds.low + half_obj_xy_dim
+    high_bounds = placement_bounds.high - half_obj_xy_dim
+    size = placement_bounds.size
+    while attempt < 1 + retries:
+        # Generate random x and y within bounds; z is determined based on model height and surface height
+        xy = rng.uniform(low_bounds, high_bounds, size=size)
+
+        # Check against all existing objects
+        overlap_found = False
+
+        # Check against other distractors and target object
+        for obj, actor_local_bbox in placed_objects:
+            if is_overlapping(
+                actor_local_bbox.min,
+                actor_local_bbox.max,
+                actor_local_bbox.min,
+                actor_local_bbox.max,
+                np.pad(xy, (0, 1)),
+                obj.pose.p,
+                padding=0.5 * minimum_aabb_separation,
+                check_z=False
+            ):
+                overlap_found = True
+                # TODO try a more intensive intersection check here if needed
+                break
+        
+        if not overlap_found:
+            return xy
+        attempt += 1
+    
+    # If no valid position found after max attempts
+    return None
+
+
+def level_to_surface(surface_height: float, bbox: ObjectBoundingBox):
+    return surface_height + 0.5 * -bbox.min[2] + 0.05
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/merge_h5.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/merge_h5.py
new file mode 100644
index 0000000000000000000000000000000000000000..1bb75ba2c574518d0029d06b819bee3396a8d89b
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/merge_h5.py
@@ -0,0 +1,184 @@
+# merge_h5_trajs.py
+import re
+import h5py
+import os
+import json
+import argparse
+from typing import List, Tuple, Optional
+
+def list_traj_keys(h5: h5py.File) -> List[str]:
+    keys = [k for k in h5.keys() if k.startswith("traj_")]
+    def ns_key(s):
+        return [int(t) if t.isdigit() else t for t in re.split(r"(\d+)", s)]
+    return sorted(keys, key=ns_key)
+
+def parse_traj_index(traj_key: str) -> Optional[int]:
+    m = re.match(r"traj_(\d+)$", traj_key)
+    return int(m.group(1)) if m else None
+
+def max_traj_index(h5: h5py.File) -> int:
+    mx = -1
+    for k in list_traj_keys(h5):
+        i = parse_traj_index(k)
+        if i is not None:
+            mx = max(mx, i)
+    return mx
+
+def create_group_with_parents(h5_or_g, path: str):
+    parts = [p for p in path.split("/") if p]
+    g = h5_or_g
+    for p in parts:
+        g = g.create_group(p) if p not in g else g[p]
+    return g
+
+def copy_attrs(src, dst):
+    for k, v in src.attrs.items():
+        dst.attrs[k] = v
+
+def copy_group_recursive(src_g: h5py.Group, dst_parent: h5py.Group, dst_name: str):
+    """Copy a group (datasets + subgroups) recursively, preserving attributes and compression."""
+    if dst_name in dst_parent:
+        raise ValueError(f"Destination already has group '{dst_name}'")
+    dst_g = dst_parent.create_group(dst_name)
+    copy_attrs(src_g, dst_g)
+
+    def _walk(src_cur: h5py.Group, rel_path=""):
+        for k, v in src_cur.items():
+            rel = f"{rel_path}/{k}" if rel_path else k
+            if isinstance(v, h5py.Group):
+                d = create_group_with_parents(dst_g, rel)
+                copy_attrs(v, d)
+                _walk(v, rel)
+            else:
+                # dataset
+                parent = create_group_with_parents(dst_g, os.path.dirname(rel)) if "/" in rel else dst_g
+                ds = parent.create_dataset(os.path.basename(rel), data=v[...], compression=v.compression)
+                copy_attrs(v, ds)
+
+    _walk(src_g)
+    return dst_g
+
+def copy_root_non_traj_items(src: h5py.File, dst: h5py.File, overwrite: bool=False):
+    """Copy root items that are not traj_*; skip collisions by default."""
+    for k, v in src.items():
+        if k.startswith("traj_"):
+            continue
+        if k in dst and not overwrite:
+            # Skip to be safe; user can merge these manually if needed.
+            continue
+        if isinstance(v, h5py.Group):
+            copy_group_recursive(v, dst, k)
+        else:
+            if k in dst and overwrite:
+                del dst[k]
+            ds = dst.create_dataset(k, data=v[...], compression=v.compression)
+            copy_attrs(v, ds)
+
+def next_renamed_key(base: str, taken: set) -> str:
+    """Generate a unique key by appending _1, _2, ..."""
+    if base not in taken:
+        return base
+    i = 1
+    while f"{base}_{i}" in taken:
+        i += 1
+    return f"{base}_{i}"
+
+def merge_two_h5(
+    a_path: str,
+    b_path: str,
+    out_path: str,
+    mode: str = "renumber",       # "renumber" or "prefix"
+    prefix_a: str = "",
+    prefix_b: str = "",
+    copy_root_meta: bool = True
+):
+    if os.path.abspath(a_path) == os.path.abspath(out_path) or os.path.abspath(b_path) == os.path.abspath(out_path):
+        raise ValueError("Output path must be different from both input files.")
+
+    with h5py.File(a_path, "r") as A, h5py.File(b_path, "r") as B, h5py.File(out_path, "w") as OUT:
+        # Copy A's trajs (optionally with prefix)
+        taken = set()
+        a_trajs = list_traj_keys(A)
+        for k in a_trajs:
+            new_k = k
+            if mode == "prefix" and prefix_a:
+                new_k = f"{prefix_a}{k}"
+            if new_k in OUT:
+                new_k = next_renamed_key(new_k, taken | set(OUT.keys()))
+            copy_group_recursive(A[k], OUT, new_k)
+            taken.add(new_k)
+
+        # Compute starting index if renumbering B
+        start_idx = max_traj_index(OUT)
+        if start_idx < 0:
+            start_idx = -1
+
+        # Copy B's trajs
+        b_trajs = list_traj_keys(B)
+        for k in b_trajs:
+            if mode == "renumber":
+                # Continue numbering after OUT's current max
+                start_idx += 1
+                new_k = f"traj_{start_idx}"
+            else:
+                # prefix mode
+                new_k = f"{prefix_b}{k}" if prefix_b else k
+                if new_k in OUT:
+                    new_k = next_renamed_key(new_k, taken | set(OUT.keys()))
+            copy_group_recursive(B[k], OUT, new_k)
+            taken.add(new_k)
+
+        # Root-level non-traj items (best-effort)
+        if copy_root_meta:
+            copy_root_non_traj_items(A, OUT, overwrite=False)
+            copy_root_non_traj_items(B, OUT, overwrite=False)
+
+        # Merge/annotate root attributes
+        OUT.attrs["source_files"] = json.dumps([os.path.abspath(a_path), os.path.abspath(b_path)])
+        OUT.attrs["merge_rule"] = (
+            "traj_* copied from A (prefix if provided), then traj_* from B "
+            f"({'renumbered sequentially' if mode=='renumber' else f'prefixed with {prefix_b!r}'})"
+        )
+
+def copy_and_write_merged_json(a_path: str, b_path: str, out_path: str):
+    """Optional: create a small metadata JSON next to the merged H5 for provenance."""
+    out_json = os.path.splitext(out_path)[0] + ".json"
+    meta = {
+        "merged_from": [os.path.abspath(a_path), os.path.abspath(b_path)],
+        "note": "This file documents the provenance of the merged HDF5."
+    }
+    try:
+        with open(out_json, "w", encoding="utf-8") as f:
+            json.dump(meta, f, indent=2)
+        print(f"Wrote merged provenance JSON -> {out_json}")
+    except Exception as e:
+        print(f"[warn] Failed to write {out_json}: {e}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Merge two HDF5 trajectory files into one.")
+    parser.add_argument("a", help="Path to first input .h5 (A)")
+    parser.add_argument("b", help="Path to second input .h5 (B)")
+    parser.add_argument("out", help="Path to output .h5")
+    parser.add_argument("--mode", choices=["renumber", "prefix"], default="renumber",
+                        help="Merging strategy for B's traj_* keys. Default: renumber to continue after A.")
+    parser.add_argument("--prefix-a", default="", help="Prefix to apply to A's traj_* when --mode=prefix")
+    parser.add_argument("--prefix-b", default="", help="Prefix to apply to B's traj_* when --mode=prefix")
+    parser.add_argument("--no-root-meta", action="store_true",
+                        help="Do not attempt to copy non-traj items at HDF5 root.")
+    parser.add_argument("--no-json", action="store_true",
+                        help="Do not write an auxiliary merged provenance JSON.")
+    args = parser.parse_args()
+
+    merge_two_h5(
+        args.a, args.b, args.out,
+        mode=args.mode,
+        prefix_a=args.prefix_a,
+        prefix_b=args.prefix_b,
+        copy_root_meta=(not args.no_root_meta),
+    )
+    if not args.no_json:
+        copy_and_write_merged_json(args.a, args.b, args.out)
+    print("Successfully merged trajectories.")
+
+if __name__ == "__main__":
+    main()
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/motionplanning.sh b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/motionplanning.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8fdcd8743c6cc8e866af37c28a70498996ab9027
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/motionplanning.sh
@@ -0,0 +1,112 @@
+#!/bin/bash
+
+# --- Configurable variables ---
+backend="cpu"
+obs_mode="rgbd"
+traj_name="box"
+num_sample=1
+num_traj=1000
+cuda=0
+nproc=5
+rand_level=2
+distractor_count=(
+  1
+  2
+  4
+  8
+  16
+  32
+)
+
+# List of tasks
+tasks=(
+    # "PickForkFromRack-v1"
+    # "PlaceSpoonOnRack-v1"
+    # "PlaceMugOnRack-v1"
+    # "PlaceMugOnCoffeeMachine-v1"
+    # "PickMugFromCoffeeMachine-v1"
+    # "PlaceBowlOnRack-v1"
+    # "PlacePlateOnRack-v1"
+    # "PlaceForkOnRack-v1"
+    # "PlaceKnifeOnRack-v1"
+    # "PlaceBowlOnRack-v2"
+    # "PlacePlateOnRack-v2"
+    # "PlaceForkOnRack-v2"
+    # "PlaceKnifeOnRack-v2"
+    # "PlaceBowlOnRack-v3"
+    # "PlacePlateOnRack-v3"
+    # "PlaceForkOnRack-v3"
+    # "PlaceKnifeOnRack-v3"
+    # "PlaceBowlOnRack-v4"
+    # "PlacePlateOnRack-v4"
+    # "PlaceForkOnRack-v4"
+    # "PlaceKnifeOnRack-v4"
+    # "GraspFork-v0"
+    # "GraspBowl-v1"
+    # "GraspPlate-v0"
+    # "GraspCup-v0"
+    # EmptyRandom-v0
+    # GraspCup-v1
+    "GraspBox-v0"
+)
+
+# List of agents
+agents=(
+  # "noahbiarm"
+  "panda"
+)
+
+# list of robot versions
+robot_uids=(
+  # "noahbiarm_r"
+  # "noahbiarm_rc"
+  # "noahbiarm_rcw"
+  "panda_wristcam"
+  # "panda"
+)
+
+# Loop over each agent
+for dc in "${distractor_count[@]}"; do
+  for agent in "${agents[@]}"; do
+    # loop over robot versions
+    for robot in "${robot_uids[@]}"; do
+      # Loop over each task for the current agent
+      for env_id in "${tasks[@]}"; do
+        echo "Processing tasks for agent: $agent (backend=$backend, obs_mode=$obs_mode, rand_level=$rand_level, traj_name=$traj_name)"
+        echo "Robot: $robot  Task: $env_id"
+
+        # # Generate sample video
+        # CUDA_VISIBLE_DEVICES="${cuda}" python -m msx_envs.data_collection.panda_run \
+        #   --distractors="$dc" \
+        #   --rand_level="$rand_level" \
+        #   --env-id="$env_id" \
+        #   --traj-name=sample.dc."$dc" \
+        #   -n="$num_sample" \
+        #   -b="$backend" \
+        #   -o="$obs_mode" \
+        #   --record-dir=/storage/backup/sajjad/data/"${robot}" \
+        #   --robot_uids="$robot" \
+        #   --num-procs=1 \
+        #   --save-video \
+        #   --only-count-success
+
+        # mv /storage/backup/sajjad/data/"${robot}"/"$env_id"/motionplanning/0.mp4 \
+        #   /storage/backup/sajjad/data/"${robot}"/"$env_id"/motionplanning/sample.dc."$dc".mp4
+
+        # Generate training data
+        CUDA_VISIBLE_DEVICES="${cuda}" python -m msx_envs.data_collection.panda_run \
+          --distractors="$dc" \
+          --rand_level="$rand_level" \
+          --env-id "$env_id" \
+          --traj-name="$traj_name".dc."$dc" \
+          --only-count-success \
+          -n "$num_traj" \
+          -b="$backend" \
+          -o="$obs_mode" \
+          --record-dir=/storage/backup/sajjad/data/"${robot}" \
+          --robot_uids="$robot" \
+          --num-procs="$nproc"
+      done
+    done
+  done
+done
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/run_uv.sbatch b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/run_uv.sbatch
new file mode 100644
index 0000000000000000000000000000000000000000..59448eab9cee9ec133534ee2a4f736b9c3282017
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/run_uv.sbatch
@@ -0,0 +1,47 @@
+#!/bin/bash -l
+#=========================================================================================
+# Slurm SBATCH Directives
+#=========================================================================================
+#SBATCH --job-name=B1kBoxSft
+#SBATCH --partition=compute                
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --gres=gpu:1 
+#SBATCH --time=7-01:00:00 
+#SBATCH --output=/shared_work/sajjad/slurm-logs/%x-%j.out
+#SBATCH --error=/shared_work/sajjad/slurm-logs/%x-%j.err
+#SBATCH --cpus-per-task=5
+#SBATCH --mem=200G 
+#=========================================================================================
+
+set -euo pipefail
+
+echo "Job started on $(hostname) at $(date)"
+
+# Clear existing module state
+module purge || true
+
+# ---- Setup UV ----
+module load uv
+
+# Explicitly set your personal paths to avoid "Permission Denied" on /pm/uv
+export UV_CACHE_DIR="/pm/sajjad/.cache/uv"
+export UV_PYTHON_INSTALL_DIR="/pm/sajjad/uv/python"
+export UV_PYTHON_BIN_DIR="/pm/sajjad/uv/bin"
+export UV_PYTHON_CACHE_DIR="/pm/sajjad/uv/python-cache"
+export UV_TOOL_DIR="/pm/sajjad/uv/tools"
+
+# ---- Activate env ----
+source /pm/sajjad/AnyBox/bin/activate
+
+# ---- Verification (Optional but helpful for logs) ----
+echo "Using Python: $(which python)"
+echo "UV Cache: $UV_CACHE_DIR"
+
+# ---- Your workload ----
+nvidia-smi || true
+
+# Run the motion planning script
+uv run --active bash /shared_work/sajjad/projects/AnyBox/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/motionplanning.sh
+
+echo "Job finished at $(date)"
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/split_h5.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/split_h5.py
new file mode 100644
index 0000000000000000000000000000000000000000..72a871792e71fd5bb03df6f00ba0e00057dea8e8
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/split_h5.py
@@ -0,0 +1,168 @@
+# split_h5_by_grasped.py
+import re
+import h5py
+import numpy as np
+import os
+import shutil
+from typing import Optional, Tuple
+
+GRASPED_PATH = "obs/extra/grasped"  # relative to each traj_i
+
+def list_traj_keys(h5: h5py.File) -> list[str]:
+    keys = [k for k in h5.keys() if k.startswith("traj_")]
+    def ns_key(s):  # natural sort
+        return [int(t) if t.isdigit() else t for t in re.split(r"(\d+)", s)]
+    return sorted(keys, key=ns_key)
+
+def find_time_length_and_datasets(group: h5py.Group) -> Tuple[Optional[int], list[Tuple[str, h5py.Dataset]]]:
+    datasets, T = [], None
+    def _walk(g: h5py.Group, prefix=""):
+        nonlocal T
+        for k, v in g.items():
+            p = f"{prefix}/{k}" if prefix else k
+            if isinstance(v, h5py.Group):
+                _walk(v, p)
+            elif isinstance(v, h5py.Dataset):
+                datasets.append((p, v))
+                if T is None and v.ndim >= 1:
+                    T = v.shape[0]
+    _walk(group)
+    return T, datasets
+
+def first_one_index(arr: np.ndarray) -> Optional[int]:
+    """
+    Return the first index t where grasped[t] == 1 (or >0.5 if float).
+    Accepts shapes (T,) or (T,1) or (T,k) -> uses column 0.
+    """
+    a = np.asarray(arr)
+    if a.ndim >= 2:
+        a = a[:, 0]
+    # normalize to bool
+    if np.issubdtype(a.dtype, np.integer):
+        mask = (a != 0)
+    elif np.issubdtype(a.dtype, np.floating):
+        mask = (a > 0.5)
+    else:
+        mask = a.astype(bool)
+    idx = np.argmax(mask)  # 0 if all False, but need to check any True
+    return int(idx) if mask.any() else None
+
+def compute_split_idx_by_grasped(traj_g: h5py.Group, fallback_T: int) -> int:
+    """
+    Use first t with grasped==1 as split index. Guards to keep both halves non-empty.
+    Fallback: middle split if grasped path missing or no 1 found.
+    """
+
+    if GRASPED_PATH in traj_g:
+        arr = traj_g[GRASPED_PATH][...]
+        flat = arr[:, 0] if arr.ndim >= 2 else arr
+        print(f"shape={flat.shape}, values={flat.tolist()}")
+    else:
+        print(f"NO {GRASPED_PATH}")
+
+    # robustly find the grasped dataset
+    if GRASPED_PATH.split("/")[0] in traj_g:
+        try:
+            grasped_ds = traj_g[GRASPED_PATH]
+            # Read lazily just once
+            first1 = first_one_index(grasped_ds[...])
+            if first1 is not None:
+                # ensure non-empty left and right sides
+                if first1 <= 0:
+                    return 1
+                if first1 >= fallback_T:
+                    return max(1, fallback_T - 1)
+                return first1
+        except Exception:
+            pass
+    # fallback: midpoint
+    return max(1, min(fallback_T - 1, fallback_T // 2))
+
+def create_group_with_parents(h5: h5py.File | h5py.Group, path: str) -> h5py.Group:
+    parts = [p for p in path.split("/") if p]
+    g = h5
+    for p in parts:
+        g = g.create_group(p) if p not in g else g[p]
+    return g
+
+def copy_attrs(src, dst):
+    for k, v in src.attrs.items():
+        dst.attrs[k] = v
+
+def write_split_traj(src_group: h5py.Group, dst_root: h5py.File, new_root_name: str, split_idx: int):
+    g0 = dst_root.create_group(f"{new_root_name}_0")
+    g1 = dst_root.create_group(f"{new_root_name}_1")
+    copy_attrs(src_group, g0); copy_attrs(src_group, g1)
+
+    def _walk_and_copy(src_g: h5py.Group, base_path=""):
+        for k, v in src_g.items():
+            rel = f"{base_path}/{k}" if base_path else k
+            if isinstance(v, h5py.Group):
+                d0 = create_group_with_parents(g0, rel)
+                d1 = create_group_with_parents(g1, rel)
+                copy_attrs(v, d0); copy_attrs(v, d1)
+                _walk_and_copy(v, rel)
+            else:
+                if v.ndim >= 1:
+                    # split along time axis
+                    arr0 = v[0:split_idx]
+                    arr1 = v[split_idx:v.shape[0]]
+                    d0 = create_group_with_parents(g0, base_path)
+                    d1 = create_group_with_parents(g1, base_path)
+                    ds0 = d0.create_dataset(k, data=arr0, compression=v.compression)
+                    ds1 = d1.create_dataset(k, data=arr1, compression=v.compression)
+                    copy_attrs(v, ds0); copy_attrs(v, ds1)
+                else:
+                    d0 = create_group_with_parents(g0, base_path)
+                    d1 = create_group_with_parents(g1, base_path)
+                    ds0 = d0.create_dataset(k, data=v[()], compression=v.compression)
+                    ds1 = d1.create_dataset(k, data=v[()], compression=v.compression)
+                    copy_attrs(v, ds0); copy_attrs(v, ds1)
+
+    _walk_and_copy(src_group)
+
+def split_all_trajs_by_grasped(src_h5_path: str, dst_h5_path: str):
+    with h5py.File(src_h5_path, "r") as src, h5py.File(dst_h5_path, "w") as dst:
+        for traj_key in list_traj_keys(src):
+            grp = src[traj_key]
+
+            try:
+                g = grp[GRASPED_PATH][...]
+                if g.ndim >= 2:
+                    g = g[:, 0]
+                g = (np.asarray(g) > 0.5).astype(np.uint8)
+                T = int(g.shape[0])
+            except Exception:
+                raise ValueError(f"Failed to read {GRASPED_PATH} for {traj_key}")
+
+            if T is None or T < 2:
+                print(f"[skip] {traj_key}: invalid T={T}")
+                continue
+
+            split_idx = compute_split_idx_by_grasped(grp, T)
+            print("split_idx:", split_idx, "T", T)
+            if split_idx < 10 or split_idx > T - 1:
+                print(f"[skip] {traj_key}: split_idx={split_idx} too close to edges")
+                continue
+            write_split_traj(grp, dst, traj_key, split_idx)
+
+        dst.attrs["source_file"] = src_h5_path
+        dst.attrs["split_rule"] = f"First 1 in {GRASPED_PATH} (fallback midpoint)."
+
+def copy_and_rename_json(src_h5_path: str, dst_h5_path: str):
+    src_json = os.path.splitext(src_h5_path)[0] + ".json"
+    dst_json = os.path.splitext(dst_h5_path)[0] + ".json"
+    if os.path.exists(src_json):
+        shutil.copyfile(src_json, dst_json)
+        print(f"Copied {src_json} -> {dst_json}")
+    else:
+        print(f"[warn] No JSON file found for {src_h5_path}")
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) < 3:
+        print("Usage: python split_h5_by_grasped.py <input.h5> <output.h5>")
+        sys.exit(1)
+    split_all_trajs_by_grasped(sys.argv[1], sys.argv[2])
+    copy_and_rename_json(sys.argv[1], sys.argv[2])
+    print("successfully split trajectories.")
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms2_envs.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms2_envs.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd0fe6c48300270013de6bb2e3e7f12471fbd26b
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms2_envs.py
@@ -0,0 +1,120 @@
+import gymnasium as gym
+from tqdm import tqdm
+import numpy as np
+import mani_skill2.envs
+import argparse
+import os
+import cv2
+from msx_envs.common import PKG_DIR, ASSETS_DIR
+
+import cfdp.envs as envs  # absolute import
+
+def print_nested_keys(d, prefix=''):
+    for key in d.keys():
+        print(f"{prefix}{key}")
+        if isinstance(d[key], dict):
+            print_nested_keys(d[key], prefix=f"{prefix} ")
+
+def save_camera_img(img, img_path):
+    # cv2.imshow("Camera Image", img)
+    # cv2.waitKey(0)
+    # Convert from RGB to BGR format for OpenCV
+    img_bgr = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    cv2.imwrite(img_path, img_bgr)
+    print(f"Saved image to {img_path}")
+
+def main():
+    # Add argument parser
+    parser = argparse.ArgumentParser(description='Motion Planning Environment Demo')
+    # parser.add_argument('--env-id', type=str, default='CustomizedPick-v0', help='Environment ID')
+    parser.add_argument('--env-id', type=str, default='ShelfPick-v0', help='Environment ID')
+    parser.add_argument('--obs-mode', type=str, default='image', help='Observation mode') #'state_dict', 'image'
+    parser.add_argument('--control-mode', type=str, default='pd_ee_delta_pose_align', 
+                       choices=['pd_joint_delta_pos', 'pd_ee_target_delta_pos', 'pd_ee_delta_pose_align'],
+                       help='Control mode')
+    parser.add_argument('--reward-mode', type=str, default='dense', help='Reward mode')
+    parser.add_argument('--robot-noise', type=float, default=0.2, help='Robot initial position noise')
+    parser.add_argument('--planner-type', type=str, default='rrt', help='Motion planner type')
+    parser.add_argument('--show-obs', action='store_true', help='Show observation keys')
+    parser.add_argument('--episodes', type=int, default=10, help='Number of episodes to run')
+    parser.add_argument('--render', action='store_true', help='Enable rendering')
+    parser.add_argument('--record', action='store_true', help='Enable video recording')
+    args = parser.parse_args()
+    # Create environment
+    env = gym.make(args.env_id,
+                   obs_mode=args.obs_mode,
+                   reward_mode=args.reward_mode,
+                   control_mode=args.control_mode,
+                   enable_shadow=False,
+                   render_mode="cameras" if args.record else "human",
+                   robot_init_qpos_noise=args.robot_noise,
+                   obstacle_configs=[],
+                #    object_config_path=os.path.join(ENVS_DIR, "env_config", "empty_scene"),
+                #    object_config_path=os.path.join(ENVS_DIR, "env_config", "smallbox_distractor"),
+                   object_config_path=os.path.join(ENVS_DIR, "env_config", "shelf"),
+                #    object_config_path=os.path.join(ENVS_DIR, "env_config", "distractor25"),
+                #    object_config_path=os.path.join(ENVS_DIR, "env_config", "distractorbox"),
+                #    object_config_path=os.path.join(ENVS_DIR, "env_config"),
+                #    object_config_path=os.path.join(ENVS_DIR, "concept_env_config"),
+                   create_obstacle_point_cloud=True,
+                   is_demo=True)
+    
+    # env = gym.make('RandomObstacleReach-v0',
+    #                obs_mode=args.obs_mode,
+    #                reward_mode=args.reward_mode,
+    #                control_mode=args.control_mode,
+    #                enable_shadow=False,
+    #                render_mode="cameras" if args.record else "human",
+    #                robot_init_qpos_noise=args.robot_noise,
+    #                obstacle_configs=[])
+
+
+    obstacle_configs = [
+        {
+            'type': 'sphere',
+            'radius': 0.18,
+            'pose': [0.6, 0.0, 0.1],
+            'color': (0, 1, 0),
+            'n_points': 500
+        }
+    ]
+
+    ##### --- Run episodes --- ####
+    obs, info = env.reset(options={"reconfigure": True})
+    if args.show_obs:
+        print("Observation structure:")
+        print_nested_keys(obs)
+    # save_camera_img(obs['image']['base_camera']['Color'], 
+    #                         f"/home/xuan/Documents/test_result/scene_images/camera_img_{episode}.png")
+    
+    import pathlib
+    save_dir = pathlib.Path("/home/xuan/Documents/test_result/scene_images")
+    save_dir.mkdir(parents=True, exist_ok=True)
+    import time
+    
+    for episode in range(args.episodes):
+        obs, info = env.reset(options={"reconfigure": True})
+        # print_nested_keys(obs)
+
+        # render_image = env.render_rgb_array()
+        # timestamp = time.strftime("%H%M%S")
+        # save_camera_img(render_image, str(save_dir / f"{timestamp}_scene_img.png"))
+        # obs_wrapper.reset()
+
+        for _ in range(100):
+            # action = env.action_space.sample()
+            # obs, reward, done, truncated, info = env.step(action)
+            
+            # obs_wrapper.update_observation(obs)
+            if args.render:
+                env.render()
+            # if done or truncated:
+            #     break
+        time.sleep(1)
+        # env.reset()
+        
+    env.close()
+    
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms3_envs.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms3_envs.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf51660d0c828c4b25962ff306232d8b0eadf29
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/test_ms3_envs.py
@@ -0,0 +1,118 @@
+import multiprocessing as mp
+import os
+from copy import deepcopy
+import time
+import argparse
+import gymnasium as gym
+import numpy as np
+from tqdm import tqdm
+import os.path as osp
+import sapien.core as sapien
+import tkinter as tk
+from mani_skill import envs
+import msx_envs
+import open3d as o3d
+
+# from mani_skill.utils.wrappers.record import RecordEpisode
+
+def parse_args(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-e", "--env-id", type=str, default="GraspBowl-v1", help=f"Environment to run motion planning solver on.")
+    parser.add_argument("-o", "--obs-mode", type=str, default="state_dict", help="Observation mode to use. Usually this is kept as 'none' as observations are not necesary to be stored, they can be replayed later via the mani_skill.trajectory.replay_trajectory script.")
+    parser.add_argument("--only-count-success", action="store_true", help="If true, generates trajectories until num_traj of them are successful and only saves the successful trajectories/videos")
+    parser.add_argument("--reward-mode", type=str)
+    parser.add_argument("-b", "--sim-backend", type=str, default="auto", help="Which simulation backend to use. Can be 'auto', 'cpu', 'gpu'")
+    parser.add_argument("--render-mode", type=str, default="human", help="can be 'sensors' or 'rgb_array' which only affect what is saved to videos")
+    parser.add_argument("--vis", action="store_true", help="whether or not to open a GUI to visualize the solution live")
+    parser.add_argument("--vis_point_cloud", action="store_true", help="whether or not to open a GUI to visualize the point cloud")
+    parser.add_argument("--shader", default="default", type=str, help="Change shader used for rendering. Default is 'default' which is very fast. Can also be 'rt' for ray tracing and generating photo-realistic renders. Can also be 'rt-fast' for a faster but lower quality ray-traced renderer")
+    parser.add_argument("--rand_level", type=int, default=4, help="the level of randomization of objects in the task")
+    parser.add_argument("--robot_uids", type=str, default="panda_sphere", help="set robot uids")
+    return parser.parse_args()
+
+def main(args):
+    env_id = args.env_id
+    print(f"---------------------------------------------{env_id}---------------------------------------------")
+    # breakpoint()
+    env = gym.make(
+        env_id,
+        obs_mode=args.obs_mode,
+        control_mode="pd_joint_delta_pos",
+        render_mode=args.render_mode,
+        reward_mode="dense",
+        sensor_configs=dict(shader_pack=args.shader),
+        human_render_camera_configs=dict(
+            shader_pack=args.shader,
+        ),
+        viewer_camera_configs=dict(shader_pack=args.shader),
+        sim_backend=args.sim_backend,
+        rand_level=args.rand_level,
+        robot_uids=args.robot_uids,
+        object_config_dir="base_configs/base_table_shelf.yaml"
+        # object_config_dir="grasp_bowl/table_box.yaml"
+    )
+    # env = gym.make(
+    #     env_id,
+    #     obs_mode=args.obs_mode,
+    #     control_mode="pd_joint_delta_pos",
+    #     render_mode=args.render_mode,
+    #     reward_mode="dense",
+    #     sensor_configs=dict(shader_pack=args.shader),
+    #     human_render_camera_configs=dict(
+    #         shader_pack=args.shader,
+    #     ),
+    #     viewer_camera_configs=dict(shader_pack=args.shader),
+    #     sim_backend=args.sim_backend,
+    #     robot_uids=args.robot_uids,
+    #     object_config_dir="base_configs/base_table.yaml"
+    #     # object_config_dir="grasp_bowl/table_box.yaml"
+    # )
+    print("robot type: ", type(env.agent.robot))
+    obs, info = env.reset(options={"reconfigure": True})
+    intrinsic = obs['sensor_param']['base_camera']['intrinsic_cv'].cpu().numpy()
+    print("intrinsic matrix: ", intrinsic)
+
+    # ---- Point cloud viz (blocking) ----
+    # if args.obs_mode == "state_dict" and "extra" in obs and "obstacle_point_cloud" in obs["extra"]:
+    #     import numpy as np
+    #     import matplotlib.pyplot as plt
+    #     from mpl_toolkits.mplot3d import Axes3D  # noqa
+
+    #     pc = obs["extra"]["obstacle_point_cloud"]
+    #     pc = pc if isinstance(pc, np.ndarray) else np.asarray(pc)
+
+    #     fig = plt.figure("Obstacle Point Cloud")
+    #     ax = fig.add_subplot(111, projection="3d")
+    #     ax.scatter(pc[:, 0], pc[:, 1], pc[:, 2], s=1)
+    #     ax.set_xlabel("X"); ax.set_ylabel("Y"); ax.set_zlabel("Z")
+    #     ax.set_box_aspect([1, 1, 1])
+
+    #     # Block here until you close the window:
+    #     plt.show()
+
+    for episode in range(100):
+        print("now at episode: ", episode)
+        obs, info = env.reset()
+        
+        for _ in range(10):
+            action = np.zeros(env.action_space.shape, dtype=env.action_space.dtype)
+            obs, reward, done, truncated, info = env.step(action)
+            # breakpoint()
+            # print("obs tcp pose shape", obs['extra']["tcp_pose"].shape)
+            # print("obs goal pose shape", obs['extra']["goal_pose"].shape)
+            # print("obs goal pos shape", obs['extra']["goal_pos"].shape)
+
+            pc = np.asarray(obs["extra"]["obstacle_point_cloud"])
+            pcd = o3d.geometry.PointCloud()
+            pcd.points = o3d.utility.Vector3dVector(pc)
+            if args.vis_point_cloud:
+                o3d.visualization.draw_geometries([pcd])
+
+            if args.vis:
+                env.render()
+        time.sleep(1)
+    env.close()
+    
+
+if __name__ == "__main__":
+    main(parse_args())
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.py b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd61ebba4df435e9b1cf2b30a5e5784bdf73b366
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.py
@@ -0,0 +1,421 @@
+"""
+Convert ManiSkill HDF5 trajectory data into a LeRobot‑compatible dataset.
+
+Usage:
+    python to_lerobot.py \
+        --hdf5-files  PATH1_TO_HDF5 PATH2_TO_HDF5 ... \
+        --env-id ENVIRONMENT_ID \
+        --robot-uid ROBOT_UID \
+        --control-mode CONTROL_MODE
+"""
+
+import dataclasses
+import shutil
+import json
+import h5py
+import torch
+import tqdm
+import tyro
+import copy
+import numpy as np
+from pathlib import Path
+from PIL import Image
+import re
+
+# from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+# # from lerobot.datasets.lerobot_dataset import LeRobotDataset
+# from lerobot.datasets.compute_stats import compute_stats
+
+# For LeRobot 0.1.0
+from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.common.datasets import compute_stats
+
+from huggingface_hub import HfApi
+
+
+@dataclasses.dataclass(frozen=True)
+class DatasetConfig:
+    use_videos: bool = True
+    tolerance_s: float = 0.0001
+    image_writer_processes: int = 10
+    image_writer_threads: int = 5
+    video_backend: str | None = None
+
+
+DEFAULT_DATASET_CONFIG = DatasetConfig()
+
+def get_motors(robot_uid:str):
+    if robot_uid == "noahbiarm":
+        motors = [
+            "Right_Joint_0",
+            "Right_Joint_1",
+            "Right_Joint_2",
+            "Right_Joint_3",
+            "Right_Joint_4",
+            "Right_Joint_5",
+            "Right_Joint_6",
+            "Right_Joint_Gripper_Up",
+            "Right_Joint_Gripper_Down",
+        ]
+
+    elif robot_uid == "noahbiarm_rc":
+        motors = [
+            "chest_rot",
+            "Right_Joint_0",
+            "Right_Joint_1",
+            "Right_Joint_2",
+            "Right_Joint_3",
+            "Right_Joint_4",
+            "Right_Joint_5",
+            "Right_Joint_6",
+            "Right_Joint_Gripper_Up",
+            "Right_Joint_Gripper_Down",
+        ]
+
+    elif robot_uid in ["panda", "panda_wristcam", "panda_sphere"]:
+        motors = [
+            "panda_joint1",
+            "panda_joint2",
+            "panda_joint3",
+            "panda_joint4",
+            "panda_joint5",
+            "panda_joint6",
+            "panda_joint7",
+            "panda_finger_joint1",
+            "panda_finger_joint2",
+        ]
+
+    return motors
+
+def get_actions(robot_uid:str, control_mode:str):
+    if robot_uid == "noahbiarm":
+        if control_mode == "pd_joint_pos":
+            acts = [
+                "Right_Joint_0",
+                "Right_Joint_1",
+                "Right_Joint_2",
+                "Right_Joint_3",
+                "Right_Joint_4",
+                "Right_Joint_5",
+                "Right_Joint_6",
+                "Right_Joint_Gripper",
+            ]
+        elif control_mode == "pd_ee_delta_pose":
+            acts = [
+                "Dx",
+                "Dy",
+                "Dz",
+                "Droll",
+                "Dpitch",
+                "Dyaw",
+                "Right_Joint_Gripper",
+            ]
+    elif robot_uid == "noahbiarm_rc":
+        if control_mode == "pd_joint_pos":
+            acts = [
+                "chest_rot",
+                "Right_Joint_0",
+                "Right_Joint_1",
+                "Right_Joint_2",
+                "Right_Joint_3",
+                "Right_Joint_4",
+                "Right_Joint_5",
+                "Right_Joint_6",
+                "Right_Joint_Gripper",
+            ]
+    elif robot_uid in ["panda", "panda_wristcam", "panda_sphere"]:
+        if control_mode == "pd_joint_pos":
+            acts = [
+                "panda_joint1",
+                "panda_joint2",
+                "panda_joint3",
+                "panda_joint4",
+                "panda_joint5",
+                "panda_joint6",
+                "panda_joint7",
+                "panda_finger",
+            ]
+
+    return acts
+
+def get_cameras(robot_uid: str):
+    if robot_uid in ["noahbiarm", "noahbiarm_rc"]:
+        cameras = [
+            "base_camera",
+            "hand_camera",
+            "head_camera"
+        ]
+
+    elif robot_uid in ["panda", "panda_wristcam", "panda_sphere"]:
+        cameras = [
+            "base_camera",
+            "hand_camera"
+        ]
+    
+    return cameras
+
+def get_dim_qpos(robot_uid:str):
+    return len(get_motors(robot_uid))
+
+def get_dim_act(robot_uid:str, control_mode:str):
+    return len(get_actions(robot_uid, control_mode))
+
+def create_empty_dataset(
+    env_id: str,
+    robot_uid: str,
+    control_mode: str,
+    mode: str,
+    dataset_config: DatasetConfig,
+) -> LeRobotDataset:
+
+    
+    D_QPOS   = get_dim_qpos(robot_uid=robot_uid)
+    D_ACT    = get_dim_act(robot_uid=robot_uid, control_mode=control_mode)
+    motors   = get_motors(robot_uid=robot_uid)
+    acts     = get_actions(robot_uid=robot_uid, control_mode=control_mode)
+    cameras  = get_cameras(robot_uid=robot_uid)
+
+    features = {
+        "observation.state": {
+            "dtype": "float32",
+            "shape": (D_QPOS,),
+            "names": [motors],
+        },
+        "action": {
+            "dtype": "float32",
+            "shape": (D_ACT,), 
+            "names": [acts],
+        },
+        "observation.box.pose": {
+            "dtype": "float32",
+            "shape": (7,), 
+            "names": [["x", "y", "z", "qx", "qy", "qz", "qw"]],
+        },
+    }
+
+    for cam in cameras:
+        features[f"observation.images.{cam}"] = {
+            "dtype": mode,
+            "shape": (3, 256, 256),  # Matches 128x128 RGB images (CHW format)
+            "names": ["channels", "height", "width"],
+        }
+        features[f"observation.depth.{cam}"] = {
+            "dtype": mode,
+            "shape": (1, 256, 256),  # Matches 128x128 RGB images (CHW format)
+            "names": ["channels", "height", "width"],
+        }
+        features[f"sensors.{cam}.extrinsic_cv"] = {
+            "dtype": "float32",
+            "shape": (3, 4), 
+            "names": ["extrinsic_cv"],
+        }
+        features[f"sensors.{cam}.intrinsic_cv"] = {
+            "dtype": "float32",
+            "shape": (3, 3), 
+            "names": ["intrinsic_cv"],
+        }
+        features[f"sensors.{cam}.cam2world_gl"] = {
+            "dtype": "float32",
+            "shape": (4, 4), 
+            "names": ["cam2world_gl"],
+        }
+
+
+    # check if data path exists
+    DATA_PATH = Path(f"/storage/backup/sajjad/data/lerobot/{robot_uid}/{control_mode}/{env_id}")
+    if (DATA_PATH).exists():
+        shutil.rmtree(DATA_PATH)
+
+    dataset = LeRobotDataset.create(
+        repo_id=env_id,
+        root=DATA_PATH,
+        fps=30,  # Assuming 50Hz;
+        robot_type=robot_uid,
+        features=features,
+        use_videos=dataset_config.use_videos,
+        tolerance_s=dataset_config.tolerance_s,
+        image_writer_processes=dataset_config.image_writer_processes,
+        image_writer_threads=dataset_config.image_writer_threads,
+        video_backend=dataset_config.video_backend,
+    )
+
+    dataset.meta.codebase_version = "2.0"
+    return dataset
+
+def load_maniskill_data_episode(f: h5py.File, traj_key: str, robot_uid: str) -> tuple[dict[str, np.ndarray], np.ndarray, np.ndarray]:
+    traj = f[traj_key]
+    obs = traj["obs"]
+    cameras = get_cameras(robot_uid=robot_uid)
+
+    # remove last env img & qpos since it has no action paired with it
+    imgs_per_cam = {
+        cam: obs["sensor_data"][cam]["rgb"][:-1]
+        for cam in cameras
+    }
+    depth_per_cam = {
+        cam: obs["sensor_data"][cam]["depth"][:-1]
+        for cam in cameras
+    }
+    extrinsic_per_cam = {
+        cam: obs["sensor_param"][cam]["extrinsic_cv"][:-1]
+        for cam in cameras
+    }
+    intrinsic_per_cam = {
+        cam: obs["sensor_param"][cam]["intrinsic_cv"][:-1]
+        for cam in cameras
+    }
+    cam2world_per_cam = {
+        cam: obs["sensor_param"][cam]["cam2world_gl"][:-1]
+        for cam in cameras
+    }
+
+    state = obs["agent"]["qpos"][:-1]
+    actions = traj["actions"]
+    box_pose = obs["extra"]["box_pose"][:-1]
+
+
+    return imgs_per_cam, depth_per_cam, \
+           extrinsic_per_cam, intrinsic_per_cam, cam2world_per_cam, \
+           state, actions, box_pose
+
+def get_task_from_hdf5(hdf5_file_name: Path) -> str:
+    # # get env name from hdf5 file
+    # name = hdf5_file_name.parts[-3]
+    # # 1. remove the version suffix like "-v1", "-v2", etc.
+    # base = re.sub(r'-v\d+$', '', name)
+    # # 2. insert a space before each capital letter (unless it's the first char)
+    # with_spaces = re.sub(r'(?<!^)(?=[A-Z])', ' ', base)
+    # # 3. lowercase the whole thing
+    # task = with_spaces.lower()
+
+    # add task instruction here
+    task = "pick up box from table and place on shelf"
+    return task
+
+def populate_dataset(
+    dataset: LeRobotDataset,
+    hdf5_file: Path,
+    env_id: str,
+    robot_uid: str,
+    starting_episode: int,
+    cur_episode: int,
+) -> LeRobotDataset:
+
+    with h5py.File(hdf5_file, "r") as f:
+        # Load JSON metadata to get episode info
+        json_path = hdf5_file.with_suffix(".json")
+        if not json_path.exists():
+            raise FileNotFoundError(f"JSON metadata file not found at {json_path}")
+        with open(json_path, "r") as json_file:
+            json_data = json.load(json_file)
+        episodes = json_data["episodes"]
+
+        total_frames = 0
+        for ep_idx, ep in enumerate(episodes):
+            cur_episode += 1
+            if cur_episode < starting_episode: continue
+            traj_key = f"traj_{ep['episode_id']}"
+            if traj_key not in f:
+                print(f"Warning: {traj_key} not found in HDF5 file, skipping")
+                continue
+
+            imgs_per_cam, depth_per_cam, \
+            extrinsic_per_cam, intrinsic_per_cam, cam2world_per_cam, \
+            state, actions, box_pose = load_maniskill_data_episode(f, traj_key, robot_uid)
+            num_frames = actions.shape[0]
+            total_frames += num_frames
+
+            print(f"Processing Episode {cur_episode} ({hdf5_file.parts[-3]} - {traj_key}) with {num_frames} frames")
+            for i in tqdm.tqdm(range(num_frames), desc=f"Episode {ep_idx}"):
+                frame = {
+                    "observation.state": torch.from_numpy(state[i]),
+                    "action": torch.from_numpy(actions[i]),
+                    "task": get_task_from_hdf5(hdf5_file),
+                    "observation.box.pose": torch.from_numpy(box_pose[i]),
+                }
+                for camera, img_array in imgs_per_cam.items():
+                    pil_img = Image.fromarray(np.array(img_array[i], dtype=np.uint8))
+                    frame[f"observation.images.{camera}"] = pil_img
+                for camera, depth_array in depth_per_cam.items():
+                    pil_img = Image.fromarray(np.squeeze(np.array(depth_array[i], dtype=np.uint8)))
+                    frame[f"observation.depth.{camera}"] = pil_img
+                for camera, extrinsic in extrinsic_per_cam.items():
+                    frame[f"sensors.{camera}.extrinsic_cv"] = torch.from_numpy(extrinsic[i])
+                for camera, intrinsic in intrinsic_per_cam.items():
+                    frame[f"sensors.{camera}.intrinsic_cv"] = torch.from_numpy(intrinsic[i])
+                for camera, cam2world in cam2world_per_cam.items():
+                    frame[f"sensors.{camera}.cam2world_gl"] = torch.from_numpy(cam2world[i])
+
+                dataset.add_frame(frame)
+            dataset.save_episode()
+            
+    return dataset, cur_episode
+
+
+def port_maniskill(
+    hdf5_files: list[Path],
+    env_id: str,
+    robot_uid: str,
+    control_mode: str,
+    mode: str = "image",
+    dataset_config: DatasetConfig = DEFAULT_DATASET_CONFIG,
+    overwrite: bool = False,
+    encode_videos: bool = False,
+    push_to_hub: bool = False,
+):
+    # check if files exist
+    for f in hdf5_files:
+        if not f.exists():
+            raise ValueError(f"HDF5 file {f} does not exist")
+
+    # create empty dataset or load from previous state
+    DATA_PATH = Path(f"data/lerobot/{robot_uid}/{control_mode}/{env_id}")
+    if (DATA_PATH).exists() and (not overwrite):
+        dataset = LeRobotDataset(
+            repo_id=DATA_PATH.name,
+            root=DATA_PATH)
+    else:
+        dataset = create_empty_dataset(
+            env_id=env_id,
+            robot_uid=robot_uid,
+            control_mode=control_mode,
+            mode=mode,
+            dataset_config=dataset_config
+        )
+
+    # loop over all h5 files and add to dataset
+    cur_episode = -1
+    starting_episode = dataset.meta.total_episodes
+    for hdf5_file in hdf5_files:
+        dataset, cur_episode = populate_dataset(
+            dataset=dataset,
+            hdf5_file=hdf5_file,
+            env_id=env_id,
+            robot_uid=robot_uid,
+            starting_episode=starting_episode,
+            cur_episode=cur_episode,
+        )
+
+    print(f"Dataset saved to {DATA_PATH}")
+
+    # # Automatically compute the stats that OpenPI needs
+    # print("Computing dataset statistics...")
+    # dataset.meta.stats = compute_stats(dataset)
+    # dataset.meta.save_meta()
+
+    # # Optionally push to the Hugging Face Hub
+    # if push_to_hub:
+    #     HfApi().create_repo(
+    #         repo_id=f"savoji/{DATA_PATH.name}",
+    #         repo_type="dataset",
+    #         private=False,        # or True if you want a private dataset
+    #         exist_ok=True         # won’t error if it already exists
+    #     )
+    #     dataset.push_to_hub(
+    #         repo_id=f"savoji/{DATA_PATH.name}",
+    #         private=False,            # or True, as desired
+    #         commit_message="initial upload"
+    #     )
+
+if __name__ == "__main__":
+    tyro.cli(port_maniskill)
\ No newline at end of file
diff --git a/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.sh b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.sh
new file mode 100644
index 0000000000000000000000000000000000000000..342840359ebce6714a3b279f50532a2a93859741
--- /dev/null
+++ b/project/ManiSkill3/src/maniskill2_benchmark/msx_envs/scripts/to_lerobot.sh
@@ -0,0 +1,47 @@
+# Define control modes
+control_modes=(
+  "pd_joint_pos"
+)
+rand_level=4
+
+# List of tasks to be performed
+tasks=(
+    # "PickForkFromRack-v1"
+    # "PlaceSpoonOnRack-v1"
+    # "PlaceMugOnRack-v1"
+    # "PlaceMugOnCoffeeMachine-v1"
+    # "PickMugFromCoffeeMachine-v1"
+    # "PlaceBowlOnRack-v1"
+    # "PlacePlateOnRack-v1"
+    # "PlaceForkOnRack-v1"
+    # "PlaceKnifeOnRack-v1"
+    # "PlaceBowlOnRack-v2"
+    # "PlacePlateOnRack-v2"
+    # "PlaceForkOnRack-v2"
+    # "PlaceKnifeOnRack-v2"
+    "PlaceBowlOnRack-v3"
+)
+
+# List of robot versions
+robot_uids=(
+  # "noahbiarm_r"
+  "noahbiarm_rc"
+  # "noahbiarm_rcw"
+)
+
+# Iterate over each robot version
+for robot in "${robot_uids[@]}"; do
+  # Iterate over each task for the current robot
+  for env_id in "${tasks[@]}"; do
+    # Iterate over each control mode
+    for control_mode in "${control_modes[@]}"; do
+      # Run the conversion script for each combination of robot, task, and control mode
+      python scripts/data_generation/to_lerobot.py \
+        --hdf5-files=data/"${robot}"/"$env_id"/motionplanning/trajectory.rl."$rand_level".h5 \
+        --env-id="$env_id"-RL"$rand_level" \
+        --robot-uid="${robot}" \
+        --control-mode="${control_mode}" \
+        --overwrite
+    done
+  done
+done
\ No newline at end of file