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project/ManiSkill3/src/maniskill3_environment/mani_skill/assets/plates/kalas_mug/kalas_mug003.obj

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project/ManiSkill3/src/maniskill3_environment/mani_skill/assets/plates/kalas_mug/kalas_mug006.obj

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project/ManiSkill3/src/maniskill3_environment/mani_skill/assets/plates/kalas_mug/kalas_mug063.obj

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project/ManiSkill3/src/maniskill3_environment/mani_skill/envs/tasks/digital_twins/__init__.py

@@ -0,0 +1 @@
+from .bridge_dataset_eval import *

project/ManiSkill3/src/maniskill3_environment/mani_skill/envs/tasks/fmb/__init__.py

@@ -0,0 +1 @@
+from .fmb import FMBAssembly1Env

project/ManiSkill3/src/maniskill3_environment/mani_skill/envs/tasks/humanoid/humanoid_pick_place.py

@@ -0,0 +1,276 @@
+import copy
+import os
+from typing import Any, Dict
+
+import numpy as np
+import sapien
+import torch
+from transforms3d.euler import euler2quat
+
+from mani_skill.agents.robots.unitree_g1.g1_upper_body import (
+    UnitreeG1UpperBodyWithHeadCamera,
+)
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.envs.utils import randomization
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils import common, sapien_utils
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.scene_builder.kitchen_counter import KitchenCounterSceneBuilder
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.structs.types import GPUMemoryConfig, SceneConfig, SimConfig
+
+
+class HumanoidPickPlaceEnv(BaseEnv):
+    SUPPORTED_REWARD_MODES = ["sparse", "none"]
+    """sets up a basic scene with a apple to pick up and place on a dish"""
+    kitchen_scene_scale = 1.0
+
+    def __init__(self, *args, robot_init_qpos_noise=0.02, **kwargs):
+        self.robot_init_qpos_noise = robot_init_qpos_noise
+        super().__init__(*args, **kwargs)
+
+    @property
+    def _default_sim_config(self):
+        return SimConfig(
+            gpu_memory_config=GPUMemoryConfig(
+                max_rigid_contact_count=2**22,
+            )
+        )
+
+    @property
+    def _default_sensor_configs(self):
+        pose = sapien_utils.look_at(eye=[0.3, 0, 0.6], target=[-0.1, 0, 0.1])
+        return [
+            CameraConfig("base_camera", pose=pose, width=128, height=128, fov=np.pi / 2)
+        ]
+
+    @property
+    def _default_human_render_camera_configs(self):
+        pose = sapien_utils.look_at([0.6, 0.7, 0.6], [0.0, 0.0, 0.35])
+        return CameraConfig("render_camera", pose=pose, width=512, height=512, fov=1)
+
+    def _load_agent(self, options: dict):
+        super()._load_agent(options, sapien.Pose(p=[0, 0, 1]))
+
+    def _load_scene(self, options: dict):
+        self.scene_builder = KitchenCounterSceneBuilder(self)
+        self.kitchen_scene = self.scene_builder.build(scale=self.kitchen_scene_scale)
+
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        self.scene_builder.initialize(env_idx)
+
+    def evaluate(self):
+        return {
+            "success": torch.zeros(self.num_envs, device=self.device, dtype=bool),
+            "fail": torch.zeros(self.num_envs, device=self.device, dtype=bool),
+        }
+
+    def _get_obs_extra(self, info: Dict):
+        return dict()
+
+
+class HumanoidPlaceAppleInBowl(HumanoidPickPlaceEnv):
+    SUPPORTED_REWARD_MODES = ["normalized_dense", "dense", "sparse", "none"]
+
+    @property
+    def _default_sensor_configs(self):
+        return CameraConfig(
+            "base_camera",
+            sapien.Pose(
+                [0.279123, 0.303438, 1.34794], [0.252428, 0.396735, 0.114442, -0.875091]
+            ),
+            128,
+            128,
+            np.pi / 2,
+            0.01,
+            100,
+        )
+
+    @property
+    def _default_human_render_camera_configs(self):
+        return CameraConfig(
+            "render_camera",
+            sapien.Pose(
+                [0.279123, 0.303438, 1.34794], [0.252428, 0.396735, 0.114442, -0.875091]
+            ),
+            512,
+            512,
+            np.pi / 2,
+            0.01,
+            100,
+        )
+
+    def _load_scene(self, options: Dict):
+        super()._load_scene(options)
+        scale = self.kitchen_scene_scale
+        builder = self.scene.create_actor_builder()
+        fix_rotation_pose = sapien.Pose(q=euler2quat(np.pi / 2, 0, 0))
+        model_dir = os.path.dirname(__file__) + "/assets"
+        builder.add_nonconvex_collision_from_file(
+            filename=os.path.join(model_dir, "frl_apartment_bowl_07.ply"),
+            pose=fix_rotation_pose,
+            scale=[scale] * 3,
+        )
+        builder.add_visual_from_file(
+            filename=os.path.join(model_dir, "frl_apartment_bowl_07.glb"),
+            scale=[scale] * 3,
+            pose=fix_rotation_pose,
+        )
+        builder.initial_pose = sapien.Pose(p=[0, -0.4, 0.753])
+        self.bowl = builder.build_kinematic(name="bowl")
+
+        builder = self.scene.create_actor_builder()
+        model_dir = os.path.dirname(__file__) + "/assets"
+        builder.add_multiple_convex_collisions_from_file(
+            filename=os.path.join(model_dir, "apple_1.ply"),
+            pose=fix_rotation_pose,
+            scale=[scale * 0.8]
+            * 3,  # scale down more to make apple a bit smaller to be graspable
+        )
+        builder.add_visual_from_file(
+            filename=os.path.join(model_dir, "apple_1.glb"),
+            scale=[scale * 0.8] * 3,
+            pose=fix_rotation_pose,
+        )
+        builder.initial_pose = sapien.Pose(p=[0, -0.4, 0.78])
+        self.apple = builder.build(name="apple")
+
+    def evaluate(self):
+        is_obj_placed = (
+            torch.linalg.norm(self.bowl.pose.p - self.apple.pose.p, axis=1) <= 0.05
+        )
+        hand_outside_bowl = (
+            self.agent.right_tcp.pose.p[:, 2] > self.bowl.pose.p[:, 2] + 0.125
+        )
+        is_grasped = self.agent.right_hand_is_grasping(self.apple, max_angle=110)
+        return {
+            "success": is_obj_placed & hand_outside_bowl,
+            "hand_outside_bowl": hand_outside_bowl,
+            "is_grasped": is_grasped,
+        }
+
+    def _get_obs_extra(self, info: Dict):
+        # in reality some people hack is_grasped into observations by checking if the gripper can close fully or not
+        obs = dict(
+            is_grasped=info["is_grasped"],
+            tcp_pose=self.agent.right_tcp.pose.raw_pose,
+        )
+        if "state" in self.obs_mode:
+            obs.update(
+                bowl_pos=self.bowl.pose.p,
+                obj_pose=self.apple.pose.raw_pose,
+                tcp_to_obj_pos=self.apple.pose.p - self.agent.right_tcp.pose.p,
+                obj_to_goal_pos=self.bowl.pose.p - self.apple.pose.p,
+            )
+        return obs
+
+    def _grasp_release_reward(self):
+        """a dense reward that rewards the agent for opening their hand"""
+        return 1 - torch.tanh(self.agent.right_hand_dist_to_open_grasp())
+
+    def compute_dense_reward(self, obs: Any, action: torch.Tensor, info: Dict):
+        tcp_to_obj_dist = torch.linalg.norm(
+            self.apple.pose.p - self.agent.right_tcp.pose.p, axis=1
+        )
+        reaching_reward = 1 - torch.tanh(5 * tcp_to_obj_dist)
+        reward = reaching_reward
+
+        is_grasped = info["is_grasped"]
+        reward += is_grasped
+
+        # encourage to bring apple to above the bowl then drop it.
+        obj_to_goal_dist = torch.linalg.norm(
+            (self.bowl.pose.p + torch.tensor([0, 0, 0.15], device=self.device))
+            - self.apple.pose.p,
+            axis=1,
+        )
+        place_reward = 1 - torch.tanh(5 * obj_to_goal_dist)
+        reward += place_reward * is_grasped
+
+        # once above the goal, encourage to have the hand above the bowl still and begin releasing the grasp
+        obj_high_above_bowl = obj_to_goal_dist < 0.025
+        grasp_release_reward = self._grasp_release_reward()
+        reward[obj_high_above_bowl] = (
+            4
+            + place_reward[obj_high_above_bowl]
+            + grasp_release_reward[obj_high_above_bowl]
+        )
+        reward[info["success"]] = (
+            8 + (place_reward + grasp_release_reward)[info["success"]]
+        )
+        return reward
+
+    def compute_normalized_dense_reward(
+        self, obs: Any, action: torch.Tensor, info: Dict
+    ):
+        return self.compute_dense_reward(obs=obs, action=action, info=info) / 10
+
+
+@register_env("UnitreeG1PlaceAppleInBowl-v1", max_episode_steps=100)
+class UnitreeG1PlaceAppleInBowlEnv(HumanoidPlaceAppleInBowl):
+    """
+    **Task Description:**
+    Control the humanoid unitree G1 robot to grab an apple with its right arm and place it in a bowl to the side
+
+    **Randomizations:**
+    - the bowl's xy position is randomized on top of a table in the region [0.025, 0.025] x [-0.025, -0.025]. It is placed flat on the table
+    - the apple's xy position is randomized on top of a table in the region [0.025, 0.025] x [-0.025, -0.025]. It is placed flat on the table
+    - the apple's z-axis rotation is randomized to a random angle
+
+    **Success Conditions:**
+    - the apple position is within 0.05m euclidean distance of the bowl's position.
+    - the robot's right hand is kept outside the bowl and is above it by at least 0.125m.
+
+    **Goal Specification:**
+    - The bowl's 3D position
+    """
+
+    _sample_video_link = "https://github.com/haosulab/ManiSkill/raw/main/figures/environment_demos/UnitreeG1PlaceAppleInBowl-v1_rt.mp4"
+
+    SUPPORTED_ROBOTS = ["unitree_g1_simplified_upper_body_with_head_camera"]
+    agent: UnitreeG1UpperBodyWithHeadCamera
+    kitchen_scene_scale = 0.82
+
+    def __init__(self, *args, **kwargs):
+        self.init_robot_pose = copy.deepcopy(
+            UnitreeG1UpperBodyWithHeadCamera.keyframes["standing"].pose
+        )
+        self.init_robot_pose.p = [-0.3, 0, 0.755]
+        super().__init__(
+            *args,
+            robot_uids="unitree_g1_simplified_upper_body_with_head_camera",
+            **kwargs
+        )
+
+    @property
+    def _default_sim_config(self):
+        return SimConfig(
+            gpu_memory_config=GPUMemoryConfig(
+                max_rigid_contact_count=2**22, max_rigid_patch_count=2**21
+            ),
+            # TODO (stao): G1 robot may need some custom collision disabling as the dextrous fingers may often be close to each other
+            # and slow down simulation. A temporary fix is to reduce contact_offset value down so that we don't check so many possible
+            # collisions
+            scene_config=SceneConfig(contact_offset=0.01),
+        )
+
+    def _initialize_episode(self, env_idx: torch.Tensor, options: Dict):
+        super()._initialize_episode(env_idx, options)
+        with torch.device(self.device):
+            b = len(env_idx)
+            # initialize the robot
+            self.agent.robot.set_qpos(self.agent.keyframes["standing"].qpos)
+            self.agent.robot.set_pose(self.init_robot_pose)
+
+            # initialize the apple to be within reach
+            xyz = torch.zeros((b, 3))
+            xyz[:, :2] = randomization.uniform(low=-0.025, high=0.025, size=(b, 2))
+            qs = randomization.random_quaternions(b, lock_x=True, lock_y=True)
+            xyz[:, 2] = 0.7335
+            self.apple.set_pose(Pose.create_from_pq(xyz, qs))
+
+            xyz = torch.zeros((b, 3))
+            xyz[:, :2] = randomization.uniform(low=-0.025, high=0.025, size=(b, 2))
+            xyz[:, :2] += torch.tensor([0.0, -0.4])
+            xyz[:, 2] = 0.753
+            self.bowl.set_pose(Pose.create_from_pq(xyz))

project/ManiSkill3/src/maniskill3_environment/mani_skill/envs/tasks/mobile_manipulation/robocasa/kitchen.py

@@ -0,0 +1,564 @@
+from copy import deepcopy
+from typing import Dict
+
+import numpy as np
+import sapien
+import torch
+
+from mani_skill.envs.sapien_env import BaseEnv
+from mani_skill.sensors.camera import CameraConfig
+from mani_skill.utils import sapien_utils
+from mani_skill.utils.registration import register_env
+from mani_skill.utils.scene_builder.robocasa.objects.kitchen_object_utils import (
+    sample_kitchen_object,
+)
+from mani_skill.utils.scene_builder.robocasa.objects.objects import MJCFObject
+from mani_skill.utils.scene_builder.robocasa.scene_builder import RoboCasaSceneBuilder
+from mani_skill.utils.scene_builder.robocasa.utils import scene_registry
+from mani_skill.utils.scene_builder.robocasa.utils.placement_samplers import (
+    RandomizationError,
+)
+from mani_skill.utils.structs.pose import Pose
+from mani_skill.utils.structs.types import GPUMemoryConfig, SimConfig
+
+
+@register_env(
+    "RoboCasaKitchen-v1", max_episode_steps=100, asset_download_ids=["RoboCasa"]
+)
+class RoboCasaKitchenEnv(BaseEnv):
+    SUPPORTED_ROBOTS = ["fetch", "none"]
+    SUPPORTED_REWARD_MODES = ["none"]
+    """
+    Initialized a Base Kitchen environment.
+
+    Args:
+        robots: Specification for specific robot arm(s) to be instantiated within this env
+            (e.g: "Sawyer" would generate one arm; ["Panda", "Panda", "Sawyer"] would generate three robot arms)
+
+        env_configuration (str): Specifies how to position the robot(s) within the environment. Default is "default",
+            which should be interpreted accordingly by any subclasses.
+
+        controller_configs (str or list of dict): If set, contains relevant controller parameters for creating a
+            custom controller. Else, uses the default controller for this specific task. Should either be single
+            dict if same controller is to be used for all robots or else it should be a list of the same length as
+            "robots" param
+
+        base_types (None or str or list of str): type of base, used to instantiate base models from base factory.
+            Default is "default", which is the default base associated with the robot(s) the 'robots' specification.
+            None results in no base, and any other (valid) model overrides the default base. Should either be
+            single str if same base type is to be used for all robots or else it should be a list of the same
+            length as "robots" param
+
+        gripper_types (None or str or list of str): type of gripper, used to instantiate
+            gripper models from gripper factory. Default is "default", which is the default grippers(s) associated
+            with the robot(s) the 'robots' specification. None removes the gripper, and any other (valid) model
+            overrides the default gripper. Should either be single str if same gripper type is to be used for all
+            robots or else it should be a list of the same length as "robots" param
+
+        initialization_noise (dict or list of dict): Dict containing the initialization noise parameters.
+            The expected keys and corresponding value types are specified below:
+
+            :`'magnitude'`: The scale factor of uni-variate random noise applied to each of a robot's given initial
+                joint positions. Setting this value to `None` or 0.0 results in no noise being applied.
+                If "gaussian" type of noise is applied then this magnitude scales the standard deviation applied,
+                If "uniform" type of noise is applied then this magnitude sets the bounds of the sampling range
+            :`'type'`: Type of noise to apply. Can either specify "gaussian" or "uniform"
+
+            Should either be single dict if same noise value is to be used for all robots or else it should be a
+            list of the same length as "robots" param
+
+            :Note: Specifying "default" will automatically use the default noise settings.
+                Specifying None will automatically create the required dict with "magnitude" set to 0.0.
+
+        use_camera_obs (bool): if True, every observation includes rendered image(s)
+
+        placement_initializer (ObjectPositionSampler): if provided, will be used to place objects on every reset,
+            else a UniformRandomSampler is used by default.
+
+        has_renderer (bool): If true, render the simulation state in
+            a viewer instead of headless mode.
+
+        has_offscreen_renderer (bool): True if using off-screen rendering
+
+        render_camera (str): Name of camera to render if `has_renderer` is True. Setting this value to 'None'
+            will result in the default angle being applied, which is useful as it can be dragged / panned by
+            the user using the mouse
+
+        render_collision_mesh (bool): True if rendering collision meshes in camera. False otherwise.
+
+        render_visual_mesh (bool): True if rendering visual meshes in camera. False otherwise.
+
+        render_gpu_device_id (int): corresponds to the GPU device id to use for offscreen rendering.
+            Defaults to -1, in which case the device will be inferred from environment variables
+            (GPUS or CUDA_VISIBLE_DEVICES).
+
+        control_freq (float): how many control signals to receive in every second. This sets the abase of
+            simulation time that passes between every action input.
+
+        horizon (int): Every episode lasts for exactly @horizon timesteps.
+
+        ignore_done (bool): True if never terminating the environment (ignore @horizon).
+
+
+        camera_names (str or list of str): name of camera to be rendered. Should either be single str if
+            same name is to be used for all cameras' rendering or else it should be a list of cameras to render.
+
+            :Note: At least one camera must be specified if @use_camera_obs is True.
+
+            :Note: To render all robots' cameras of a certain type (e.g.: "robotview" or "eye_in_hand"), use the
+                convention "all-{name}" (e.g.: "all-robotview") to automatically render all camera images from each
+                robot's camera list).
+
+        camera_heights (int or list of int): height of camera frame. Should either be single int if
+            same height is to be used for all cameras' frames or else it should be a list of the same length as
+            "camera names" param.
+
+        camera_widths (int or list of int): width of camera frame. Should either be single int if
+            same width is to be used for all cameras' frames or else it should be a list of the same length as
+            "camera names" param.
+
+        camera_depths (bool or list of bool): True if rendering RGB-D, and RGB otherwise. Should either be single
+            bool if same depth setting is to be used for all cameras or else it should be a list of the same length as
+            "camera names" param.
+
+        renderer (str): Specifies which renderer to use.
+
+        renderer_config (dict): dictionary for the renderer configurations
+
+        init_robot_base_pos (str): name of the fixture to place the near. If None, will randomly select a fixture.
+
+        seed (int): environment seed. Default is None, where environment is unseeded, ie. random
+
+        layout_and_style_ids (list of list of int): list of layout and style ids to use for the kitchen.
+
+        layout_ids ((list of) LayoutType or int):  layout id(s) to use for the kitchen. -1 and None specify all layouts
+            -2 specifies layouts not involving islands/wall stacks, -3 specifies layouts involving islands/wall stacks,
+            -4 specifies layouts with dining areas.
+
+        style_ids ((list of) StyleType or int): style id(s) to use for the kitchen. -1 and None specify all styles.
+
+        generative_textures (str): if set to "100p", will use AI generated textures
+
+        obj_registries (tuple of str): tuple containing the object registries to use for sampling objects.
+            can contain "objaverse" and/or "aigen" to sample objects from objaverse, AI generated, or both.
+
+        obj_instance_split (str): string for specifying a custom set of object instances to use. "A" specifies
+            all but the last 3 object instances (or the first half - whichever is larger), "B" specifies the
+            rest, and None specifies all.
+
+        use_distractors (bool): if True, will add distractor objects to the scene
+
+        translucent_robot (bool): if True, will make the robot appear translucent during rendering
+
+        randomize_cameras (bool): if True, will add gaussian noise to the position and rotation of the
+            wrist and agentview cameras
+    """
+
+    EXCLUDE_LAYOUTS = []
+
+    def __init__(
+        self,
+        *args,
+        robot_uids="fetch",
+        env_configuration="default",
+        controller_configs=None,
+        gripper_types="default",
+        base_types="default",
+        initialization_noise="default",
+        use_camera_obs=True,
+        use_object_obs=True,  # currently unused variable
+        reward_scale=1.0,  # currently unused variable
+        reward_shaping=False,  # currently unused variables
+        placement_initializer=None,
+        has_renderer=False,
+        has_offscreen_renderer=True,
+        render_camera="robot0_agentview_center",
+        render_collision_mesh=False,
+        render_visual_mesh=True,
+        render_gpu_device_id=-1,
+        control_freq=20,
+        horizon=1000,
+        ignore_done=False,
+        # hard_reset=True,
+        camera_names="agentview",
+        camera_heights=256,
+        camera_widths=256,
+        camera_depths=False,
+        renderer="mujoco",
+        renderer_config=None,
+        init_robot_base_pos=None,
+        seed=None,
+        layout_and_style_ids=None,
+        layout_ids=None,
+        style_ids=None,
+        scene_split=None,  # unsued, for backwards compatibility
+        generative_textures=None,
+        obj_registries=("objaverse",),
+        obj_instance_split=None,
+        use_distractors=False,
+        translucent_robot=False,
+        randomize_cameras=False,
+        fixtures_only=False,
+        **kwargs,
+    ):
+        self.init_robot_base_pos = init_robot_base_pos
+
+        # object placement initializer
+        self.placement_initializer = placement_initializer
+        self.obj_registries = obj_registries
+        self.obj_instance_split = obj_instance_split
+
+        if layout_and_style_ids is not None:
+            assert (
+                layout_ids is None and style_ids is None
+            ), "layout_ids and style_ids must both be set to None if layout_and_style_ids is set"
+            self.layout_and_style_ids = layout_and_style_ids
+        else:
+            layout_ids = scene_registry.unpack_layout_ids(layout_ids)
+            style_ids = scene_registry.unpack_style_ids(style_ids)
+            self.layout_and_style_ids = [(l, s) for l in layout_ids for s in style_ids]
+
+        # remove excluded layouts
+        self.layout_and_style_ids = [
+            (int(l), int(s))
+            for (l, s) in self.layout_and_style_ids
+            if l not in self.EXCLUDE_LAYOUTS
+        ]
+
+        assert generative_textures in [None, False, "100p"]
+        self.generative_textures = generative_textures
+
+        self.use_distractors = use_distractors
+        self.translucent_robot = translucent_robot
+        self.randomize_cameras = randomize_cameras
+
+        # intialize cameras
+        # self._cam_configs = deepcopy(CamUtils.CAM_CONFIGS)
+
+        self.use_object_obs = use_object_obs
+        self.reward_scale = reward_scale
+        self.reward_shaping = reward_shaping
+
+        ### code from original robocasa env class ###
+        self._ep_meta = {}
+        self.fixtures_only = fixtures_only
+        super().__init__(*args, robot_uids=robot_uids, **kwargs)
+
+    @property
+    def _default_sim_config(self):
+        return SimConfig(spacing=8, control_freq=20)
+
+    @property
+    def _default_sensor_configs(self):
+        # TODO (fix cameras to be where robocasa places them)
+        pose = sapien_utils.look_at([3.0, -7.5, 2.5], [3.0, 0.0, 1.0])
+        return [
+            CameraConfig("base_camera", pose, 128, 128, 60 * np.pi / 180, 0.01, 100)
+        ]
+
+    @property
+    def _default_human_render_camera_configs(self):
+        pose = sapien_utils.look_at([3.0, -7.5, 2.5], [3.0, 0.0, 1.0])
+        return CameraConfig(
+            "render_camera", pose, 2048, 2048, 60 * np.pi / 180, 0.01, 100
+        )
+
+    @property
+    def _default_viewer_camera_config(self):
+        return CameraConfig(
+            uid="viewer",
+            pose=sapien.Pose([0, 0, 1]),
+            width=1920,
+            height=1080,
+            shader_pack="default",
+            near=0.0,
+            far=1000,
+            fov=60 * np.pi / 180,
+        )
+
+    def _load_agent(self, options: dict):
+        ps = torch.zeros((self.num_envs, 3), device=self.device)
+        ps[:, 2] = 5
+        super()._load_agent(options, Pose.create_from_pq(p=ps))
+
+    def _load_scene(self, options: dict):
+        self.scene_builder = RoboCasaSceneBuilder(self)
+        self.scene_builder.build()
+        # self.fixtures = data["fixtures"]
+        # self.actors = data["actors"]
+        # self.fixture_configs = data["fixture_configs"]
+        self.fixture_refs = []
+        self.objects = []
+        self.object_cfgs = []
+        self.object_actors = []
+        for _ in range(self.num_envs):
+            self.fixture_refs.append({})
+            self.objects.append({})
+            self.object_cfgs.append({})
+            self.object_actors.append({})
+
+        # hacky way to ensure robocasa task classes can be easily imported into maniskill
+        # by manually setting into self the current scene idx to be loaded and checking if _get_obj_cfgs exists
+        if not self.fixtures_only and hasattr(self, "_get_obj_cfgs"):
+            for scene_idx in range(self.num_envs):
+                self._scene_idx_to_be_loaded = scene_idx
+                self._setup_kitchen_references()
+
+                # add objects
+                def _create_obj(cfg):
+                    if "info" in cfg:
+                        """
+                        if cfg has "info" key in it, that means it is storing meta data already
+                        that indicates which object we should be using.
+                        set the obj_groups to this path to do deterministic playback
+                        """
+                        mjcf_path = cfg["info"]["mjcf_path"]
+                        # replace with correct base path
+                        new_base_path = os.path.join(
+                            robocasa.models.assets_root, "objects"
+                        )
+                        new_path = os.path.join(
+                            new_base_path, mjcf_path.split("/objects/")[-1]
+                        )
+                        obj_groups = new_path
+                        exclude_obj_groups = None
+                    else:
+                        obj_groups = cfg.get("obj_groups", "all")
+                        exclude_obj_groups = cfg.get("exclude_obj_groups", None)
+                    object_kwargs, object_info = self.sample_object(
+                        obj_groups,
+                        exclude_groups=exclude_obj_groups,
+                        graspable=cfg.get("graspable", None),
+                        washable=cfg.get("washable", None),
+                        microwavable=cfg.get("microwavable", None),
+                        cookable=cfg.get("cookable", None),
+                        freezable=cfg.get("freezable", None),
+                        max_size=cfg.get("max_size", (None, None, None)),
+                        object_scale=cfg.get("object_scale", None),
+                        rng=self._batched_episode_rng[scene_idx],
+                    )
+                    if "name" not in cfg:
+                        cfg["name"] = "obj_{}".format(obj_num + 1)
+                    info = object_info
+                    object = MJCFObject(self.scene, name=cfg["name"], **object_kwargs)
+                    return object, info
+
+                for _ in range(10):
+                    objects = {}
+                    if "object_cfgs" in self._ep_meta:
+                        object_cfgs = self._ep_meta["object_cfgs"]
+                        for obj_num, cfg in enumerate(object_cfgs):
+                            model, info = _create_obj(cfg)
+                            cfg["info"] = info
+                            objects[model.name] = model
+                            # self.model.merge_objects([model])
+                    else:
+                        object_cfgs = self._get_obj_cfgs()
+                        addl_obj_cfgs = []
+                        for obj_num, cfg in enumerate(object_cfgs):
+                            cfg["type"] = "object"
+                            model, info = _create_obj(cfg)
+                            cfg["info"] = info
+                            objects[model.name] = model
+                            # self.model.merge_objects([model])
+
+                            try_to_place_in = cfg["placement"].get(
+                                "try_to_place_in", None
+                            )
+
+                            # place object in a container and add container as an object to the scene
+                            if try_to_place_in and (
+                                "in_container"
+                                in cfg["info"]["groups_containing_sampled_obj"]
+                            ):
+                                container_cfg = {}
+                                container_cfg["name"] = cfg["name"] + "_container"
+                                container_cfg["obj_groups"] = try_to_place_in
+                                container_cfg["placement"] = deepcopy(cfg["placement"])
+                                container_cfg["type"] = "object"
+
+                                container_kwargs = cfg["placement"].get(
+                                    "container_kwargs", None
+                                )
+                                if container_kwargs is not None:
+                                    for k, v in container_kwargs.values():
+                                        container_cfg[k] = v
+
+                                # add in the new object to the model
+                                addl_obj_cfgs.append(container_cfg)
+                                model, info = _create_obj(container_cfg)
+                                container_cfg["info"] = info
+                                objects[model.name] = model
+                                # self.model.merge_objects([model])
+
+                                # modify object config to lie inside of container
+                                cfg["placement"] = dict(
+                                    size=(0.01, 0.01),
+                                    ensure_object_boundary_in_range=False,
+                                    sample_args=dict(
+                                        reference=container_cfg["name"],
+                                    ),
+                                )
+
+                        # prepend the new object configs in
+                        object_cfgs = addl_obj_cfgs + object_cfgs
+
+                        # # remove objects that didn't get created
+                        # self.object_cfgs = [cfg for cfg in self.object_cfgs if "model" in cfg]
+                    self.object_cfgs[scene_idx] = object_cfgs
+                    self.objects[scene_idx] = objects
+                    placement_initializer = (
+                        self.scene_builder._get_placement_initializer(
+                            self.scene_builder.scene_data[self._scene_idx_to_be_loaded][
+                                "fixtures"
+                            ],
+                            objects,
+                            object_cfgs,
+                            rng=self._batched_episode_rng[scene_idx],
+                        )
+                    )
+
+                    object_placements = None
+                    for i in range(10):
+                        try:
+                            object_placements = placement_initializer.sample(
+                                placed_objects=self.scene_builder.scene_data[
+                                    self._scene_idx_to_be_loaded
+                                ]["fxtr_placements"]
+                            )
+                        except RandomizationError:
+                            #     if macros.VERBOSE:
+                            #         print("Ranomization error in initial placement. Try #{}".format(i))
+                            continue
+
+                        break
+                    if object_placements is None:
+                        print("Could not place objects. Trying again with new objects")
+                        continue
+                    for obj_pos, obj_quat, obj in object_placements.values():
+                        obj.pos = obj_pos
+                        obj.quat = obj_quat
+                        actor = obj.build(scene_idxs=[scene_idx]).actor
+                        self.object_actors[scene_idx][obj.name] = {
+                            "actor": actor,
+                            "pose": sapien.Pose(obj_pos, obj_quat),
+                        }
+                    break
+
+    def _initialize_episode(self, env_idx: torch.Tensor, options: dict):
+        with torch.device(self.device):
+            self.scene_builder.initialize(env_idx)
+            # TODO (stao): no way to make this faster atm on GPU without a lot of work
+            for scene_idx in range(self.num_envs):
+                for actor_data in self.object_actors[scene_idx].values():
+                    actor_data["actor"].set_pose(actor_data["pose"])
+
+    def evaluate(self):
+        return {}
+
+    def _get_obs_extra(self, info: Dict):
+        return dict()
+
+    """
+    Code below is for leveraging the RoboCasa objects dataset and sampling it
+    """
+
+    def _setup_kitchen_references(self):
+        """
+        setup fixtures (and their references). this function is called within load_model function for kitchens
+        """
+        serialized_refs = self._ep_meta.get("fixture_refs", {})
+        # unserialize refs
+        self.fixture_refs[self._scene_idx_to_be_loaded] = {
+            k: self.scene_builder.get_fixture(
+                self.scene_builder.scene_data[self._scene_idx_to_be_loaded]["fixtures"],
+                v,
+            )
+            for (k, v) in serialized_refs.items()
+        }
+
+    def register_fixture_ref(self, ref_name, fn_kwargs):
+        """
+        Registers a fixture reference for later use. Initializes the fixture
+        if it has not been initialized yet.
+
+        Args:
+            ref_name (str): name of the reference
+
+            fn_kwargs (dict): keyword arguments to pass to get_fixture
+
+        Returns:
+            Fixture: fixture object
+        """
+        if ref_name not in self.fixture_refs:
+            scene_idx = self._scene_idx_to_be_loaded
+            self.fixture_refs[scene_idx][ref_name] = self.scene_builder.get_fixture(
+                self.scene_builder.scene_data[scene_idx]["fixtures"], **fn_kwargs
+            )
+        return self.fixture_refs[scene_idx][ref_name]
+
+    def sample_object(
+        self,
+        groups,
+        exclude_groups=None,
+        graspable=None,
+        microwavable=None,
+        washable=None,
+        cookable=None,
+        freezable=None,
+        split=None,
+        obj_registries=None,
+        max_size=(None, None, None),
+        object_scale=None,
+        rng=None,
+    ):
+        """
+        Sample a kitchen object from the specified groups and within max_size bounds.
+
+        Args:
+            groups (list or str): groups to sample from or the exact xml path of the object to spawn
+
+            exclude_groups (str or list): groups to exclude
+
+            graspable (bool): whether the sampled object must be graspable
+
+            washable (bool): whether the sampled object must be washable
+
+            microwavable (bool): whether the sampled object must be microwavable
+
+            cookable (bool): whether whether the sampled object must be cookable
+
+            freezable (bool): whether whether the sampled object must be freezable
+
+            split (str): split to sample from. Split "A" specifies all but the last 3 object instances
+                        (or the first half - whichever is larger), "B" specifies the  rest, and None
+                        specifies all.
+
+            obj_registries (tuple): registries to sample from
+
+            max_size (tuple): max size of the object. If the sampled object is not within bounds of max size,
+                            function will resample
+
+            object_scale (float): scale of the object. If set will multiply the scale of the sampled object by this value
+
+
+        Returns:
+            dict: kwargs to apply to the MJCF model for the sampled object
+
+            dict: info about the sampled object - the path of the mjcf, groups which the object's category belongs to,
+            the category of the object the sampling split the object came from, and the groups the object was sampled from
+        """
+        return sample_kitchen_object(
+            groups,
+            exclude_groups=exclude_groups,
+            graspable=graspable,
+            washable=washable,
+            microwavable=microwavable,
+            cookable=cookable,
+            freezable=freezable,
+            rng=rng,
+            obj_registries=(obj_registries or self.obj_registries),
+            split=(split or self.obj_instance_split),
+            max_size=max_size,
+            object_scale=object_scale,
+        )