feat(pi05): add continuous 6D SE3 actions

src/lerobot/datasets/compute_stats.py

@@ -20,7 +20,11 @@ import logging
 import numpy as np
 import torch
 
-from lerobot.processor import RelativeActionsProcessorStep, to_relative_actions
+from lerobot.processor import (
+    RelativeActionsProcessorStep,
+    relative_action_output_dim,
+    to_relative_actions,
+)
 from lerobot.utils.constants import ACTION, OBS_STATE
 
 from .io_utils import load_image_as_numpy
@@ -666,26 +670,24 @@ def _compute_relative_chunk_batch(
 ) -> np.ndarray:
     """Vectorised relative-action computation for a batch of start indices.
 
-    Returns an ``(N * chunk_size, action_dim)`` float32 array.
+    Returns an ``(N * chunk_size, model_action_dim)`` float32 array.
     """
     if len(start_indices) == 0:
-        return np.empty((0, all_actions.shape[1]), dtype=np.float32)
+        output_dim = relative_action_output_dim(all_actions.shape[1], pose_representation, se3_pose_groups)
+        return np.empty((0, output_dim), dtype=np.float32)
     offsets = np.arange(chunk_size)
     frame_idx = start_indices[:, None] + offsets[None, :]
     chunks = all_actions[frame_idx].copy()
     states = all_states[start_indices]
-    if pose_representation == "se3":
+    if pose_representation in {"se3", "se3_6d"}:
-        return (
+        converted = to_relative_actions(
-            to_relative_actions(
+            torch.from_numpy(chunks),
-                torch.from_numpy(chunks),
+            torch.from_numpy(states),
-                torch.from_numpy(states),
+            relative_mask.astype(bool).tolist(),
-                relative_mask.astype(bool).tolist(),
+            pose_representation=pose_representation,
-                pose_representation=pose_representation,
+            se3_pose_groups=se3_pose_groups,
-                se3_pose_groups=se3_pose_groups,
-            )
-            .numpy()
-            .reshape(-1, all_actions.shape[1])
         )
+        return converted.numpy().reshape(-1, converted.shape[-1])
     mask_dim = len(relative_mask)
     chunks[:, :, :mask_dim] -= states[:, None, :mask_dim] * relative_mask[None, None, :]
     return chunks.reshape(-1, all_actions.shape[1])

src/lerobot/datasets/dataset_tools.py

@@ -1596,8 +1596,9 @@ def recompute_stats(
         state_history_steps: Number of consecutive synthesized state samples.
         relative_state_history: Express state history relative to its newest pose.
         relative_state_exclude_joints: State dimensions to retain as absolute.
-        relative_pose_representation: ``componentwise`` for legacy subtraction or
+        relative_pose_representation: ``componentwise`` for legacy subtraction,
-            ``se3`` for ``inv(T_current) @ T_target`` pose composition.
+            ``se3`` for composition with an axis-angle output, or ``se3_6d`` for
+            composition with a continuous two-column rotation output.
         relative_se3_pose_groups: Six-index xyz+rotation-vector pose groups.
 
     Returns:

src/lerobot/policies/pi05/configuration_pi05.py

@@ -56,6 +56,8 @@ class PI05Config(PreTrainedConfig):
     # Populated at runtime from dataset metadata by make_policy.
     action_feature_names: list[str] | None = None
     # ``se3`` uses inv(T_current) @ T_target for each xyz+rotation-vector pose group.
+    # ``se3_6d`` uses the same composition and expands each relative rotation
+    # vector to the continuous two-column 6-D rotation representation.
     # ``componentwise`` preserves the legacy action - state behavior.
     relative_pose_representation: str = "componentwise"
     relative_se3_pose_groups: list[list[int]] = field(default_factory=lambda: [list(range(6))])
@@ -136,16 +138,21 @@ class PI05Config(PreTrainedConfig):
         if self.proprioception_history_steps < 1:
             raise ValueError("proprioception_history_steps must be at least 1")
 
-        if self.relative_pose_representation not in {"componentwise", "se3"}:
+        if self.relative_pose_representation not in {"componentwise", "se3", "se3_6d"}:
             raise ValueError(
-                "relative_pose_representation must be either 'componentwise' or 'se3', got "
+                "relative_pose_representation must be 'componentwise', 'se3', or 'se3_6d', got "
                 f"{self.relative_pose_representation!r}"
             )
         for group in self.relative_se3_pose_groups:
             if len(group) != 6 or len(set(group)) != 6 or any(index < 0 for index in group):
                 raise ValueError(f"Invalid six-index SE(3) pose group: {group}")
-        if self.relative_pose_representation == "se3" and not self.relative_se3_pose_groups:
+            if self.relative_pose_representation == "se3_6d" and group != list(range(group[0], group[0] + 6)):
-            raise ValueError("relative_pose_representation='se3' requires relative_se3_pose_groups")
+                raise ValueError("se3_6d pose groups must contain six contiguous ascending indices")
+        if self.relative_pose_representation in {"se3", "se3_6d"} and not self.relative_se3_pose_groups:
+            raise ValueError(
+                f"relative_pose_representation={self.relative_pose_representation!r} "
+                "requires relative_se3_pose_groups"
+            )
 
     def validate_features(self) -> None:
         """Validate and set up input/output features."""
@@ -163,6 +170,29 @@ class PI05Config(PreTrainedConfig):
                 shape=(self.max_action_dim,),  # Padded to max_action_dim
             )
             self.output_features[ACTION] = action_feature
+        elif self.relative_pose_representation == "se3_6d":
+            action_feature = self.output_features[ACTION]
+            source_dim = (
+                len(self.action_feature_names)
+                if self.action_feature_names is not None
+                else action_feature.shape[-1]
+            )
+            model_dim = source_dim + 3 * len(self.relative_se3_pose_groups)
+            if action_feature.shape[-1] == source_dim:
+                self.output_features[ACTION] = PolicyFeature(
+                    type=action_feature.type,
+                    shape=(model_dim,),
+                )
+            elif action_feature.shape[-1] != model_dim:
+                raise ValueError(
+                    "se3_6d action feature has incompatible width: "
+                    f"source={source_dim}, expected model width={model_dim}, "
+                    f"got={action_feature.shape[-1]}"
+                )
+            if model_dim > self.max_action_dim:
+                raise ValueError(
+                    f"se3_6d action width {model_dim} exceeds max_action_dim={self.max_action_dim}"
+                )
 
         if OBS_STATE not in self.input_features:
             state_shape = (self.max_state_dim,)

src/lerobot/policies/pi05/processor_pi05.py

@@ -36,6 +36,7 @@ from lerobot.processor import (
     TokenizerProcessorStep,
     UnnormalizerProcessorStep,
     policy_action_to_transition,
+    relative_action_output_dim,
     to_relative_actions,
     transition_to_policy_action,
 )
@@ -143,12 +144,6 @@ class Pi05FlattenStateHistoryProcessorStep(ProcessorStep):
                 f"Expected state history with shape (B, {self.history_steps}, D), got {state.shape}"
             )
 
-        flattened_dim = state.shape[1] * state.shape[2]
-        if flattened_dim > self.max_state_dim:
-            raise ValueError(
-                f"Flattened state history has {flattened_dim} dimensions, above max_state_dim={self.max_state_dim}"
-            )
-
         processed_state = state.clone()
         if self.relative:
             mask_step = RelativeActionsProcessorStep(
@@ -164,6 +159,12 @@ class Pi05FlattenStateHistoryProcessorStep(ProcessorStep):
                 se3_pose_groups=self.se3_pose_groups,
             )
 
+        flattened_dim = processed_state.shape[1] * processed_state.shape[2]
+        if flattened_dim > self.max_state_dim:
+            raise ValueError(
+                f"Flattened state history has {flattened_dim} dimensions, above max_state_dim={self.max_state_dim}"
+            )
+
         new_transition = transition.copy()
         new_observation = dict(observation)
         new_observation[OBS_STATE] = processed_state.flatten(start_dim=1)
@@ -187,8 +188,22 @@ class Pi05FlattenStateHistoryProcessorStep(ProcessorStep):
         transformed = deepcopy(features)
         for feature_group in transformed.values():
             state_feature = feature_group.get(OBS_STATE)
-            if state_feature is not None and self.history_steps > 1:
+            if state_feature is not None:
-                state_dim = state_feature.shape[-1] * self.history_steps
+                state_dim = state_feature.shape[-1]
+                if self.relative:
+                    source_dim = len(self.state_names) if self.state_names is not None else state_dim
+                    model_dim = relative_action_output_dim(
+                        source_dim,
+                        self.pose_representation,
+                        self.se3_pose_groups,
+                    )
+                    if state_dim == source_dim:
+                        state_dim = model_dim
+                    elif state_dim != model_dim:
+                        raise ValueError(
+                            f"Expected source/model state width {source_dim}/{model_dim}, got {state_dim}"
+                        )
+                state_dim *= self.history_steps
                 feature_group[OBS_STATE] = PolicyFeature(type=state_feature.type, shape=(state_dim,))
         return transformed
 

src/lerobot/processor/__init__.py

@@ -89,10 +89,15 @@ from .policy_robot_bridge import (
 from .relative_action_processor import (
     AbsoluteActionsProcessorStep,
     RelativeActionsProcessorStep,
+    relative_action_output_dim,
+    rotation_6d_to_rotvec,
+    rotvec_to_rotation_6d,
     to_absolute_actions,
     to_absolute_se3_pose,
+    to_absolute_se3_pose_6d,
     to_relative_actions,
     to_relative_se3_pose,
+    to_relative_se3_pose_6d,
 )
 from .rename_processor import RenameObservationsProcessorStep, rename_stats
 from .tokenizer_processor import ActionTokenizerProcessorStep, TokenizerProcessorStep
@@ -137,10 +142,15 @@ __all__ = [
     "make_default_robot_observation_processor",
     "AbsoluteActionsProcessorStep",
     "RelativeActionsProcessorStep",
+    "relative_action_output_dim",
+    "rotation_6d_to_rotvec",
+    "rotvec_to_rotation_6d",
     "to_absolute_actions",
     "to_absolute_se3_pose",
+    "to_absolute_se3_pose_6d",
     "to_relative_actions",
     "to_relative_se3_pose",
+    "to_relative_se3_pose_6d",
     "MapDeltaActionToRobotActionStep",
     "MapTensorToDeltaActionDictStep",
     "NewLineTaskProcessorStep",

src/lerobot/processor/relative_action_processor.py

@@ -21,7 +21,7 @@ from torch import Tensor
 
 from lerobot.configs import PipelineFeatureType, PolicyFeature
 from lerobot.types import EnvTransition, TransitionKey
-from lerobot.utils.constants import OBS_STATE
+from lerobot.utils.constants import ACTION, OBS_STATE
 
 from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
 from .pipeline import ProcessorStep, ProcessorStepRegistry
@@ -36,6 +36,11 @@ __all__ = [
     "to_absolute_actions",
     "to_relative_se3_pose",
     "to_absolute_se3_pose",
+    "to_relative_se3_pose_6d",
+    "to_absolute_se3_pose_6d",
+    "rotation_6d_to_rotvec",
+    "rotvec_to_rotation_6d",
+    "relative_action_output_dim",
 ]
 
 
@@ -85,6 +90,102 @@ def _quaternion_rotate(quaternion: Tensor, vector: Tensor) -> Tensor:
     return vector + 2.0 * (quaternion[..., :1] * uv + uuv)
 
 
+def _quaternion_to_matrix(quaternion: Tensor) -> Tensor:
+    quaternion = quaternion / torch.linalg.vector_norm(quaternion, dim=-1, keepdim=True).clamp_min(1e-12)
+    w, x, y, z = quaternion.unbind(-1)
+    two_s = 2.0
+    return torch.stack(
+        (
+            1.0 - two_s * (y * y + z * z),
+            two_s * (x * y - z * w),
+            two_s * (x * z + y * w),
+            two_s * (x * y + z * w),
+            1.0 - two_s * (x * x + z * z),
+            two_s * (y * z - x * w),
+            two_s * (x * z - y * w),
+            two_s * (y * z + x * w),
+            1.0 - two_s * (x * x + y * y),
+        ),
+        dim=-1,
+    ).reshape(quaternion.shape[:-1] + (3, 3))
+
+
+def _matrix_to_quaternion(matrix: Tensor) -> Tensor:
+    """Convert proper rotation matrices to normalized ``[w, x, y, z]`` quaternions."""
+    if matrix.shape[-2:] != (3, 3):
+        raise ValueError(f"Rotation matrices must have shape (..., 3, 3), got {matrix.shape}")
+
+    m00 = matrix[..., 0, 0]
+    m01 = matrix[..., 0, 1]
+    m02 = matrix[..., 0, 2]
+    m10 = matrix[..., 1, 0]
+    m11 = matrix[..., 1, 1]
+    m12 = matrix[..., 1, 2]
+    m20 = matrix[..., 2, 0]
+    m21 = matrix[..., 2, 1]
+    m22 = matrix[..., 2, 2]
+
+    # Each row is a quaternion candidate scaled by the magnitude of its
+    # best-conditioned component. Selecting the largest component avoids the
+    # trace singularity at rotations close to pi.
+    q_abs = torch.sqrt(
+        torch.clamp(
+            torch.stack(
+                (
+                    1.0 + m00 + m11 + m22,
+                    1.0 + m00 - m11 - m22,
+                    1.0 - m00 + m11 - m22,
+                    1.0 - m00 - m11 + m22,
+                ),
+                dim=-1,
+            ),
+            min=0.0,
+        )
+    )
+    quat_by_rijk = torch.stack(
+        (
+            torch.stack((q_abs[..., 0].square(), m21 - m12, m02 - m20, m10 - m01), dim=-1),
+            torch.stack((m21 - m12, q_abs[..., 1].square(), m10 + m01, m02 + m20), dim=-1),
+            torch.stack((m02 - m20, m10 + m01, q_abs[..., 2].square(), m12 + m21), dim=-1),
+            torch.stack((m10 - m01, m02 + m20, m12 + m21, q_abs[..., 3].square()), dim=-1),
+        ),
+        dim=-2,
+    )
+    candidates = quat_by_rijk / (2.0 * q_abs[..., :, None].clamp_min(0.1))
+    best = torch.nn.functional.one_hot(q_abs.argmax(dim=-1), num_classes=4).to(dtype=matrix.dtype)
+    quaternion = (candidates * best[..., :, None]).sum(dim=-2)
+    return quaternion / torch.linalg.vector_norm(quaternion, dim=-1, keepdim=True).clamp_min(1e-12)
+
+
+def rotvec_to_rotation_6d(rotvec: Tensor) -> Tensor:
+    """Encode an axis-angle rotation as two rotation-matrix columns."""
+    matrix = _quaternion_to_matrix(_rotvec_to_quaternion(rotvec))
+    return torch.cat((matrix[..., :, 0], matrix[..., :, 1]), dim=-1)
+
+
+def rotation_6d_to_rotvec(rotation_6d: Tensor) -> Tensor:
+    """Decode two predicted 3-D vectors into an axis-angle rotation.
+
+    Gram-Schmidt orthonormalization follows the continuous 6-D rotation
+    representation. Degenerate predictions fail closed instead of producing an
+    invalid physical rotation.
+    """
+    if rotation_6d.shape[-1] != 6:
+        raise ValueError(f"6-D rotations must have six values, got {rotation_6d.shape}")
+    first = rotation_6d[..., :3]
+    second = rotation_6d[..., 3:]
+    first_norm = torch.linalg.vector_norm(first, dim=-1, keepdim=True)
+    first_unit = first / first_norm.clamp_min(1e-12)
+    second_orthogonal = second - (first_unit * second).sum(dim=-1, keepdim=True) * first_unit
+    second_norm = torch.linalg.vector_norm(second_orthogonal, dim=-1, keepdim=True)
+    if bool(torch.any(first_norm <= 1e-8)) or bool(torch.any(second_norm <= 1e-8)):
+        raise ValueError("Cannot decode a degenerate 6-D rotation prediction")
+    second_unit = second_orthogonal / second_norm
+    third_unit = torch.linalg.cross(first_unit, second_unit, dim=-1)
+    matrix = torch.stack((first_unit, second_unit, third_unit), dim=-1)
+    return _quaternion_to_rotvec(_matrix_to_quaternion(matrix))
+
+
 def to_relative_se3_pose(target_pose: Tensor, reference_pose: Tensor) -> Tensor:
     """Encode a pose as ``inv(T_reference) @ T_target``.
 
@@ -116,6 +217,22 @@ def to_absolute_se3_pose(relative_pose: Tensor, reference_pose: Tensor) -> Tenso
     return torch.cat((target_translation, _quaternion_to_rotvec(target_quaternion)), dim=-1)
 
 
+def to_relative_se3_pose_6d(target_pose: Tensor, reference_pose: Tensor) -> Tensor:
+    """Encode ``inv(T_reference) @ T_target`` as xyz plus continuous 6-D rotation."""
+    relative_pose = to_relative_se3_pose(target_pose, reference_pose)
+    return torch.cat((relative_pose[..., :3], rotvec_to_rotation_6d(relative_pose[..., 3:])), dim=-1)
+
+
+def to_absolute_se3_pose_6d(relative_pose: Tensor, reference_pose: Tensor) -> Tensor:
+    """Decode xyz plus continuous 6-D rotation with ``T_target = T_reference @ T_relative``."""
+    if relative_pose.shape[-1] != 9:
+        raise ValueError("6-D encoded SE(3) poses must have nine values: xyz plus rotation-6D")
+    relative_rotvec_pose = torch.cat(
+        (relative_pose[..., :3], rotation_6d_to_rotvec(relative_pose[..., 3:])), dim=-1
+    )
+    return to_absolute_se3_pose(relative_rotvec_pose, reference_pose)
+
+
 def _broadcast_reference(actions: Tensor, state: Tensor) -> Tensor:
     if state.device != actions.device or state.dtype != actions.dtype:
         state = state.to(device=actions.device, dtype=actions.dtype)
@@ -130,14 +247,17 @@ def _validate_se3_pose_groups(
     mask: Sequence[bool],
     action_dim: int,
 ) -> list[list[int]]:
-    if pose_representation not in {"componentwise", "se3"}:
+    if pose_representation not in {"componentwise", "se3", "se3_6d"}:
         raise ValueError(
-            f"Unsupported pose_representation={pose_representation!r}; expected 'componentwise' or 'se3'"
+            f"Unsupported pose_representation={pose_representation!r}; expected "
+            "'componentwise', 'se3', or 'se3_6d'"
         )
     if pose_representation == "componentwise":
         return []
     if not se3_pose_groups:
-        raise ValueError("pose_representation='se3' requires at least one six-index se3_pose_group")
+        raise ValueError(
+            f"pose_representation={pose_representation!r} requires at least one six-index se3_pose_group"
+        )
 
     normalized_groups: list[list[int]] = []
     used_indices: set[int] = set()
@@ -147,6 +267,8 @@ def _validate_se3_pose_groups(
             raise ValueError(f"Each SE(3) pose group must contain six indices, got {group}")
         if len(set(group)) != 6 or any(index < 0 or index >= action_dim for index in group):
             raise ValueError(f"Invalid SE(3) pose group for action_dim={action_dim}: {group}")
+        if pose_representation == "se3_6d" and group != list(range(group[0], group[0] + 6)):
+            raise ValueError("se3_6d pose groups must contain six contiguous ascending indices")
         if any(index >= len(mask) for index in group):
             raise ValueError(f"SE(3) pose group lies outside the relative mask: {group}")
         if used_indices.intersection(group):
@@ -160,6 +282,68 @@ def _validate_se3_pose_groups(
     return normalized_groups
 
 
+def relative_action_output_dim(
+    source_dim: int,
+    pose_representation: str,
+    se3_pose_groups: Sequence[Sequence[int]] | None,
+) -> int:
+    """Return the model-space action width for a source action width."""
+    if pose_representation != "se3_6d":
+        return source_dim
+    groups = se3_pose_groups or []
+    return source_dim + 3 * len(groups)
+
+
+def _expand_se3_6d_actions(
+    actions: Tensor,
+    state: Tensor,
+    groups: Sequence[Sequence[int]],
+) -> Tensor:
+    group_by_start = {group[0]: list(group) for group in groups}
+    grouped_indices = {index for group in groups for index in group}
+    parts: list[Tensor] = []
+    for index in range(actions.shape[-1]):
+        group = group_by_start.get(index)
+        if group is not None:
+            parts.append(to_relative_se3_pose_6d(actions[..., group], state[..., group]))
+        elif index not in grouped_indices:
+            parts.append(actions[..., index : index + 1])
+    return torch.cat(parts, dim=-1)
+
+
+def _collapse_se3_6d_actions(
+    actions: Tensor,
+    state: Tensor,
+    mask: Sequence[bool],
+    groups: Sequence[Sequence[int]],
+) -> Tensor:
+    source_dim = len(mask)
+    expected_dim = relative_action_output_dim(source_dim, "se3_6d", groups)
+    if actions.shape[-1] != expected_dim:
+        raise ValueError(
+            f"Expected se3_6d action width {expected_dim} for source width {source_dim}, "
+            f"got {actions.shape[-1]}"
+        )
+    group_by_start = {group[0]: list(group) for group in groups}
+    grouped_indices = {index for group in groups for index in group}
+    parts: list[Tensor] = []
+    cursor = 0
+    for index in range(source_dim):
+        group = group_by_start.get(index)
+        if group is not None:
+            parts.append(to_absolute_se3_pose_6d(actions[..., cursor : cursor + 9], state[..., group]))
+            cursor += 9
+        elif index not in grouped_indices:
+            value = actions[..., cursor : cursor + 1]
+            if mask[index]:
+                value = value + state[..., index : index + 1]
+            parts.append(value)
+            cursor += 1
+    if cursor != actions.shape[-1]:
+        raise RuntimeError(f"Consumed {cursor} action values from width {actions.shape[-1]}")
+    return torch.cat(parts, dim=-1)
+
+
 def to_relative_actions(
     actions: Tensor,
     state: Tensor,
@@ -170,8 +354,10 @@ def to_relative_actions(
 ) -> Tensor:
     """Convert absolute actions to a configured relative representation.
 
-    Component-wise mode computes ``action - state``. SE(3) mode computes
+    Component-wise mode computes ``action - state``. SE(3) modes compute
-    ``inv(T_state) @ T_action`` for each configured pose group.
+    ``inv(T_state) @ T_action`` for each configured pose group. ``se3_6d``
+    replaces each three-value relative rotation vector with its continuous
+    six-value encoding, increasing the output width by three per pose group.
 
     Args:
         actions: (B, T, action_dim) or (B, action_dim).
@@ -190,6 +376,8 @@ def to_relative_actions(
     state_offset = state[..., :dims] * component_mask
     actions = actions.clone()
     actions[..., :dims] -= state_offset
+    if pose_representation == "se3_6d":
+        return _expand_se3_6d_actions(actions, state, groups)
     for group in groups:
         actions[..., group] = to_relative_se3_pose(actions[..., group], state[..., group])
     return actions
@@ -213,7 +401,12 @@ def to_absolute_actions(
         state: (B, state_dim). Broadcast across time dimension.
         mask: Which dims to convert. Can be shorter than action_dim.
     """
-    groups = _validate_se3_pose_groups(pose_representation, se3_pose_groups, mask, actions.shape[-1])
+    source_dim = len(mask)
+    groups = _validate_se3_pose_groups(pose_representation, se3_pose_groups, mask, source_dim)
+    state = _broadcast_reference(actions, state)
+    if pose_representation == "se3_6d":
+        return _collapse_se3_6d_actions(actions, state, mask, groups)
+
     mask_t = torch.tensor(mask, dtype=actions.dtype, device=actions.device)
     dims = mask_t.shape[0]
     # Align state to the same device/dtype as actions. _last_state is cached before
@@ -253,6 +446,7 @@ class RelativeActionsProcessorStep(ProcessorStep):
     pose_representation: str = "componentwise"
     se3_pose_groups: list[list[int]] = field(default_factory=list)
     _last_state: torch.Tensor | None = field(default=None, init=False, repr=False)
+    _last_mask: list[bool] | None = field(default=None, init=False, repr=False)
 
     def _build_mask(self, action_dim: int) -> list[bool]:
         if not self.exclude_joints or self.action_names is None:
@@ -284,6 +478,7 @@ class RelativeActionsProcessorStep(ProcessorStep):
         # Always cache state for the paired AbsoluteActionsProcessorStep
         if reference_state is not None:
             self._last_state = reference_state
+            self._last_mask = self._build_mask(reference_state.shape[-1])
 
         if not self.enabled:
             return transition
@@ -293,7 +488,7 @@ class RelativeActionsProcessorStep(ProcessorStep):
         if action is None or reference_state is None:
             return new_transition
 
-        mask = self._build_mask(action.shape[-1])
+        mask = self._last_mask or self._build_mask(action.shape[-1])
         new_transition[TransitionKey.ACTION] = to_relative_actions(
             action,
             reference_state,
@@ -307,6 +502,15 @@ class RelativeActionsProcessorStep(ProcessorStep):
         """Return the cached ``observation.state`` used as the reference point for relative/absolute action conversions."""
         return self._last_state
 
+    def get_cached_mask(self) -> list[bool] | None:
+        """Return the source-space mask cached with the latest state."""
+        return self._last_mask
+
+    def reset(self) -> None:
+        """Drop the inference reference so it cannot leak between sessions."""
+        self._last_state = None
+        self._last_mask = None
+
     def get_config(self) -> dict[str, Any]:
         return {
             "enabled": self.enabled,
@@ -319,7 +523,26 @@ class RelativeActionsProcessorStep(ProcessorStep):
     def transform_features(
         self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
     ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
-        return features
+        if not self.enabled or self.pose_representation != "se3_6d":
+            return features
+        transformed = {feature_type: dict(feature_group) for feature_type, feature_group in features.items()}
+        for feature_group in transformed.values():
+            action_feature = feature_group.get(ACTION)
+            if action_feature is None:
+                continue
+            source_dim = len(self.action_names) if self.action_names is not None else action_feature.shape[-1]
+            model_dim = relative_action_output_dim(source_dim, self.pose_representation, self.se3_pose_groups)
+            if action_feature.shape[-1] == source_dim:
+                feature_group[ACTION] = PolicyFeature(
+                    type=action_feature.type,
+                    shape=(model_dim,),
+                )
+            elif action_feature.shape[-1] != model_dim:
+                raise ValueError(
+                    f"Expected source/model action width {source_dim}/{model_dim}, "
+                    f"got {action_feature.shape[-1]}"
+                )
+        return transformed
 
 
 @ProcessorStepRegistry.register("absolute_actions_processor")
@@ -361,7 +584,9 @@ class AbsoluteActionsProcessorStep(ProcessorStep):
         if action is None:
             return new_transition
 
-        mask = self.relative_step._build_mask(action.shape[-1])
+        mask = self.relative_step.get_cached_mask()
+        if mask is None:
+            mask = self.relative_step._build_mask(cached_state.shape[-1])
         new_transition[TransitionKey.ACTION] = to_absolute_actions(
             action,
             cached_state,

src/lerobot/scripts/lerobot_train.py

@@ -343,6 +343,8 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                 "enabled": True,
                 "exclude_joints": getattr(active_cfg, "relative_exclude_joints", []),
                 "action_names": getattr(active_cfg, "action_feature_names", None),
+                "pose_representation": getattr(active_cfg, "relative_pose_representation", "componentwise"),
+                "se3_pose_groups": getattr(active_cfg, "relative_se3_pose_groups", []),
             }
             postprocessor_overrides["absolute_actions_processor"] = {"enabled": True}
         processor_kwargs["preprocessor_overrides"] = preprocessor_overrides

tests/processor/test_pi05_state_from_action.py

@@ -22,6 +22,7 @@ import torch
 
 pytest.importorskip("transformers")
 
+from lerobot.configs import FeatureType, PolicyFeature  # noqa: E402
 from lerobot.datasets.compute_stats import (  # noqa: E402
     compute_relative_action_stats,
     compute_state_history_stats,
@@ -36,7 +37,7 @@ from lerobot.processor.relative_action_processor import (  # noqa: E402
     RelativeActionsProcessorStep,
 )
 from lerobot.types import TransitionKey  # noqa: E402
-from lerobot.utils.constants import OBS_STATE  # noqa: E402
+from lerobot.utils.constants import ACTION, OBS_STATE  # noqa: E402
 
 
 def _transition(action: torch.Tensor | None, state: torch.Tensor | None = None) -> dict:
@@ -63,6 +64,27 @@ def test_pi05_config_requests_action_history_prefix():
     assert config.action_delta_indices == [-1, 0, 1, 2, 3]
 
 
+def test_pi05_config_accepts_se3_6d_action_and_state_with_two_step_history():
+    names = ["x", "y", "z", "rx", "ry", "rz", "gripper_width"]
+    config = PI05Config(
+        device="cpu",
+        use_relative_actions=True,
+        state_from_action=True,
+        proprioception_history_steps=2,
+        use_relative_state_history=True,
+        relative_pose_representation="se3_6d",
+        action_feature_names=names,
+        output_features={
+            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
+        },
+    )
+
+    config.validate_features()
+
+    assert config.output_features[ACTION].shape == (10,)
+    assert config.input_features[OBS_STATE].shape == (10,)
+
+
 def test_state_from_action_extracts_history_and_preserves_target_horizon():
     action = torch.arange(2 * 5 * 3, dtype=torch.float32).reshape(2, 5, 3)
     step = Pi05StateFromActionProcessorStep(enabled=True, history_steps=2)
@@ -87,6 +109,16 @@ def test_relative_actions_use_newest_state_in_history_and_roundtrip():
     torch.testing.assert_close(recovered[TransitionKey.ACTION], absolute)
 
 
+def test_relative_action_reference_is_reset_between_inference_sessions():
+    step = RelativeActionsProcessorStep(enabled=True)
+    step(_transition(None, torch.tensor([[1.0, 2.0]])))
+
+    step.reset()
+
+    assert step.get_cached_state() is None
+    assert step.get_cached_mask() is None
+
+
 def test_flatten_state_history_preserves_chronological_order():
     state_history = torch.tensor([[[1.0, 2.0], [3.0, 4.0]]])
     step = Pi05FlattenStateHistoryProcessorStep(history_steps=2, max_state_dim=4)
@@ -136,6 +168,27 @@ def test_state_history_can_use_se3_composition_with_absolute_gripper():
     torch.testing.assert_close(result[TransitionKey.OBSERVATION][OBS_STATE], expected, atol=1e-6, rtol=1e-6)
 
 
+def test_state_history_can_use_se3_6d_rotation_with_absolute_gripper():
+    state_history = torch.tensor(
+        [[[0.0, 1.0, 0.0, 0.0, 0.0, pi / 2, 0.2], [0.0, 0.0, 0.0, 0.0, 0.0, pi / 2, 0.4]]]
+    )
+    step = Pi05FlattenStateHistoryProcessorStep(
+        history_steps=2,
+        max_state_dim=20,
+        relative=True,
+        exclude_joints=["gripper"],
+        state_names=["x", "y", "z", "rx", "ry", "rz", "gripper_width"],
+        pose_representation="se3_6d",
+        se3_pose_groups=[list(range(6))],
+    )
+
+    result = step(_transition(torch.zeros(1, 2, 7), state_history))
+
+    identity_6d = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0]
+    expected = torch.tensor([[1.0, 0.0, 0.0, *identity_6d, 0.2, 0.0, 0.0, 0.0, *identity_6d, 0.4]])
+    torch.testing.assert_close(result[TransitionKey.OBSERVATION][OBS_STATE], expected, atol=1e-6, rtol=1e-6)
+
+
 def test_inference_state_history_is_rolled_and_reset():
     step = Pi05StateFromActionProcessorStep(enabled=True, history_steps=2)
 
@@ -225,3 +278,44 @@ def test_se3_relative_action_stats_use_reference_frame():
 
     np.testing.assert_allclose(stats["mean"][:3], [0.5, 0.0, 0.0], atol=1e-6)
     np.testing.assert_allclose(stats["mean"][6], 0.25, atol=1e-6)
+
+
+def test_se3_6d_stats_expand_action_and_state_history():
+    actions = np.asarray(
+        [
+            [0.0, 0.0, 0.0, 0.0, 0.0, pi / 2, 0.2],
+            [0.0, 1.0, 0.0, 0.0, 0.0, pi / 2, 0.3],
+        ],
+        dtype=np.float32,
+    )
+    dataset = {"action": actions, "episode_index": np.zeros(2, dtype=np.int64)}
+    features = {
+        "action": {
+            "shape": [7],
+            "names": ["x", "y", "z", "rx", "ry", "rz", "gripper_width"],
+        }
+    }
+
+    action_stats = compute_relative_action_stats(
+        dataset,
+        features,
+        chunk_size=2,
+        exclude_joints=["gripper"],
+        state_from_action=True,
+        pose_representation="se3_6d",
+        se3_pose_groups=[list(range(6))],
+    )
+    state_stats = compute_state_history_stats(
+        dataset,
+        features,
+        history_steps=2,
+        exclude_joints=["gripper"],
+        relative=True,
+        pose_representation="se3_6d",
+        se3_pose_groups=[list(range(6))],
+    )
+
+    assert action_stats["mean"].shape == (10,)
+    assert state_stats["mean"].shape == (20,)
+    np.testing.assert_allclose(action_stats["mean"][:3], [0.5, 0.0, 0.0], atol=1e-6)
+    np.testing.assert_allclose(action_stats["mean"][9], 0.25, atol=1e-6)

tests/processor/test_se3_relative_actions.py

@@ -4,10 +4,14 @@ import pytest
 import torch
 
 from lerobot.processor.relative_action_processor import (
+    rotation_6d_to_rotvec,
+    rotvec_to_rotation_6d,
     to_absolute_actions,
     to_absolute_se3_pose,
+    to_absolute_se3_pose_6d,
     to_relative_actions,
     to_relative_se3_pose,
+    to_relative_se3_pose_6d,
 )
 
 POSE_GROUP = [list(range(6))]
@@ -68,3 +72,63 @@ def test_se3_pose_group_cannot_be_partially_relative():
             pose_representation="se3",
             se3_pose_groups=POSE_GROUP,
         )
+
+
+@pytest.mark.parametrize(
+    "rotvec",
+    [
+        [0.0, 0.0, 0.0],
+        [0.2, -0.5, 0.8],
+        [math.pi - 1e-4, 0.0, 0.0],
+    ],
+)
+def test_rotation_6d_roundtrip(rotvec):
+    source = torch.tensor([rotvec], dtype=torch.float64)
+
+    recovered = rotation_6d_to_rotvec(rotvec_to_rotation_6d(source))
+
+    torch.testing.assert_close(recovered, source, atol=2e-6, rtol=2e-6)
+
+
+def test_se3_6d_pose_roundtrip_for_batched_chunks():
+    torch.manual_seed(1)
+    reference = torch.randn(4, 6)
+    reference[:, 3:] *= 0.8
+    target = torch.randn(4, 11, 6)
+    target[..., 3:] *= 0.8
+
+    relative = to_relative_se3_pose_6d(target, reference.unsqueeze(1))
+    recovered = to_absolute_se3_pose_6d(relative, reference.unsqueeze(1))
+
+    assert relative.shape == (4, 11, 9)
+    torch.testing.assert_close(recovered, target, atol=2e-5, rtol=2e-5)
+
+
+def test_mixed_se3_6d_pose_and_absolute_gripper_roundtrip():
+    reference = torch.tensor([[0.2, -0.1, 0.4, 0.1, 0.2, -0.3, 0.06]])
+    target = torch.tensor([[[0.3, 0.2, 0.5, -0.2, 0.1, 0.4, 0.03], [0.1, -0.3, 0.2, 0.5, -0.1, 0.2, 0.05]]])
+    mask = [True, True, True, True, True, True, False]
+
+    relative = to_relative_actions(
+        target,
+        reference,
+        mask,
+        pose_representation="se3_6d",
+        se3_pose_groups=POSE_GROUP,
+    )
+    recovered = to_absolute_actions(
+        relative,
+        reference,
+        mask,
+        pose_representation="se3_6d",
+        se3_pose_groups=POSE_GROUP,
+    )
+
+    assert relative.shape == (1, 2, 10)
+    torch.testing.assert_close(relative[..., 9], target[..., 6])
+    torch.testing.assert_close(recovered, target, atol=2e-5, rtol=2e-5)
+
+
+def test_rotation_6d_rejects_degenerate_prediction():
+    with pytest.raises(ValueError, match="degenerate"):
+        rotation_6d_to_rotvec(torch.zeros(1, 6))