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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
import torch
from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
from lerobot.utils.constants import OBS_IMAGES, OBS_PREFIX, OBS_STATE, OBS_STR
from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
@dataclass
@ProcessorStepRegistry.register(name="libero_processor")
class LiberoProcessorStep(ObservationProcessorStep):
"""
Processes LIBERO observations into the LeRobot format.
This step handles the specific observation structure from LIBERO environments,
which includes nested robot_state dictionaries and image observations.
**State Processing:**
- Processes the `robot_state` dictionary which contains nested end-effector,
gripper, and joint information.
- Extracts and concatenates:
- End-effector position (3D)
- End-effector quaternion converted to axis-angle (3D)
- Gripper joint positions (2D)
- Maps the concatenated state to `"observation.state"`.
**Image Processing:**
- Rotates images by 180 degrees by flipping both height and width dimensions.
- This accounts for the HuggingFaceVLA/libero camera orientation convention.
"""
def _process_observation(self, observation):
"""
Processes both image and robot_state observations from LIBERO.
"""
processed_obs = observation.copy()
for key in list(processed_obs.keys()):
if key.startswith(f"{OBS_IMAGES}."):
img = processed_obs[key]
# Flip both H and W
img = torch.flip(img, dims=[2, 3])
processed_obs[key] = img
# Process robot_state into a flat state vector
observation_robot_state_str = OBS_PREFIX + "robot_state"
if observation_robot_state_str in processed_obs:
robot_state = processed_obs.pop(observation_robot_state_str)
# Extract components
eef_pos = robot_state["eef"]["pos"] # (B, 3,)
eef_quat = robot_state["eef"]["quat"] # (B, 4,)
gripper_qpos = robot_state["gripper"]["qpos"] # (B, 2,)
# Convert quaternion to axis-angle
eef_axisangle = self._quat2axisangle(eef_quat) # (B, 3)
# Concatenate into a single state vector
state = torch.cat((eef_pos, eef_axisangle, gripper_qpos), dim=-1)
# ensure float32
state = state.float()
if state.dim() == 1:
state = state.unsqueeze(0)
processed_obs[OBS_STATE] = state
return processed_obs
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""
Transforms feature keys from the LIBERO format to the LeRobot standard.
"""
new_features: dict[PipelineFeatureType, dict[str, PolicyFeature]] = {}
# copy over non-STATE features
for ft, feats in features.items():
if ft != FeatureType.STATE:
new_features[ft] = feats.copy()
# rebuild STATE features
state_feats = {}
# add our new flattened state
state_feats[OBS_STATE] = PolicyFeature(
type=FeatureType.STATE,
shape=(8,), # [eef_pos(3), axis_angle(3), gripper(2)]
)
new_features[FeatureType.STATE] = state_feats
return new_features
def observation(self, observation):
return self._process_observation(observation)
def _quat2axisangle(self, quat: torch.Tensor) -> torch.Tensor:
"""
Convert batched quaternions to axis-angle format.
Only accepts torch tensors of shape (B, 4).
Args:
quat (Tensor): (B, 4) tensor of quaternions in (x, y, z, w) format
Returns:
Tensor: (B, 3) axis-angle vectors
Raises:
TypeError: if input is not a torch tensor
ValueError: if shape is not (B, 4)
"""
if not isinstance(quat, torch.Tensor):
raise TypeError(f"_quat2axisangle expected a torch.Tensor, got {type(quat)}")
if quat.ndim != 2 or quat.shape[1] != 4:
raise ValueError(f"_quat2axisangle expected shape (B, 4), got {tuple(quat.shape)}")
quat = quat.to(dtype=torch.float32)
device = quat.device
batch_size = quat.shape[0]
w = quat[:, 3].clamp(-1.0, 1.0)
den = torch.sqrt(torch.clamp(1.0 - w * w, min=0.0))
result = torch.zeros((batch_size, 3), device=device)
mask = den > 1e-10
if mask.any():
angle = 2.0 * torch.acos(w[mask]) # (M,)
axis = quat[mask, :3] / den[mask].unsqueeze(1)
result[mask] = axis * angle.unsqueeze(1)
return result
@dataclass
@ProcessorStepRegistry.register(name="isaaclab_arena_processor")
class IsaaclabArenaProcessorStep(ObservationProcessorStep):
"""
Processes IsaacLab Arena observations into LeRobot format.
**State Processing:**
- Extracts state components from obs["policy"] based on `state_keys`.
- Concatenates into a flat vector mapped to "observation.state".
**Image Processing:**
- Extracts images from obs["camera_obs"] based on `camera_keys`.
- Converts from (B, H, W, C) uint8 to (B, C, H, W) float32 [0, 1].
- Maps to "observation.images.<camera_name>".
"""
# Configurable from IsaacLabEnv config / cli args: --env.state_keys="robot_joint_pos,left_eef_pos"
state_keys: tuple[str, ...]
# Configurable from IsaacLabEnv config / cli args: --env.camera_keys="robot_pov_cam_rgb"
camera_keys: tuple[str, ...]
def _process_observation(self, observation):
"""
Processes both image and policy state observations from IsaacLab Arena.
"""
processed_obs = {}
if f"{OBS_STR}.camera_obs" in observation:
camera_obs = observation[f"{OBS_STR}.camera_obs"]
for cam_name, img in camera_obs.items():
if cam_name not in self.camera_keys:
continue
img = img.permute(0, 3, 1, 2).contiguous()
if img.dtype == torch.uint8:
img = img.float() / 255.0
elif img.dtype != torch.float32:
img = img.float()
processed_obs[f"{OBS_IMAGES}.{cam_name}"] = img
# Process policy state -> observation.state
if f"{OBS_STR}.policy" in observation:
policy_obs = observation[f"{OBS_STR}.policy"]
# Collect state components in order
state_components = []
for key in self.state_keys:
if key in policy_obs:
component = policy_obs[key]
# Flatten extra dims: (B, N, M) -> (B, N*M)
if component.dim() > 2:
batch_size = component.shape[0]
component = component.view(batch_size, -1)
state_components.append(component)
if state_components:
state = torch.cat(state_components, dim=-1)
state = state.float()
processed_obs[OBS_STATE] = state
return processed_obs
def transform_features(
self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
"""Not used for policy evaluation."""
return features
def observation(self, observation):
return self._process_observation(observation)
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