refactor(processor): clarify action types, distinguish PolicyAction, RobotAction, and EnvAction (#1908)

* refactor(processor): split action from policy, robots and environment

- Updated function names to robot_action_to_transition and robot_transition_to_action across multiple files to better reflect their purpose in processing robot actions.
- Adjusted references in the RobotProcessorPipeline and related components to ensure compatibility with the new naming convention.
- Enhanced type annotations for action parameters to improve code readability and maintainability.

* refactor(converters): rename robot_transition_to_action to transition_to_robot_action

- Updated function names across multiple files to improve clarity and consistency in processing robot actions.
- Adjusted references in RobotProcessorPipeline and related components to align with the new naming convention.
- Simplified action handling in the AddBatchDimensionProcessorStep by removing unnecessary checks for action presence.

* refactor(converters): update references to transition_to_robot_action

- Renamed all instances of robot_transition_to_action to transition_to_robot_action across multiple files for consistency and clarity in the processing of robot actions.
- Adjusted the RobotProcessorPipeline configurations to reflect the new naming convention, enhancing code readability.

* refactor(processor): update Torch2NumpyActionProcessorStep to extend ActionProcessorStep

- Changed the base class of Torch2NumpyActionProcessorStep from PolicyActionProcessorStep to ActionProcessorStep, aligning it with the current architecture of action processing.
- This modification enhances the clarity of the class's role in the processing pipeline.

* fix(processor): main action processor can take also EnvAction

---------

Co-authored-by: Steven Palma <steven.palma@huggingface.co>

examples/phone_to_so100/evaluate.py

@@ -25,7 +25,7 @@ from lerobot.processor import RobotProcessorPipeline
 from lerobot.processor.converters import (
     identity_transition,
     observation_to_transition,
-    transition_to_action,
+    transition_to_robot_action,
 )
 from lerobot.record import record_loop
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
@@ -76,7 +76,7 @@ robot_ee_to_joints_processor = RobotProcessorPipeline(
         ),
     ],
     to_transition=identity_transition,
-    to_output=transition_to_action,
+    to_output=transition_to_robot_action,
 )
 
 # Build pipeline to convert joint observation to ee pose observation

examples/phone_to_so100/record.py

@@ -22,10 +22,10 @@ from lerobot.datasets.utils import combine_feature_dicts
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import RobotProcessorPipeline
 from lerobot.processor.converters import (
-    action_to_transition,
     identity_transition,
     observation_to_transition,
-    transition_to_action,
+    robot_action_to_transition,
+    transition_to_robot_action,
 )
 from lerobot.record import record_loop
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
@@ -89,7 +89,7 @@ phone_to_robot_ee_pose_processor = RobotProcessorPipeline(
             max_ee_twist_step_rad=0.50,
         ),
     ],
-    to_transition=action_to_transition,
+    to_transition=robot_action_to_transition,
     to_output=identity_transition,
 )
 
@@ -107,7 +107,7 @@ robot_ee_to_joints_processor = RobotProcessorPipeline(
         ),
     ],
     to_transition=identity_transition,
-    to_output=transition_to_action,
+    to_output=transition_to_robot_action,
 )
 
 # Build pipeline to convert joint observation to ee pose observation

examples/phone_to_so100/replay.py

@@ -20,7 +20,7 @@ import time
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import RobotProcessorPipeline
-from lerobot.processor.converters import action_to_transition, transition_to_action
+from lerobot.processor.converters import robot_action_to_transition, transition_to_robot_action
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.robot_kinematic_processor import (
     AddRobotObservationAsComplimentaryData,
@@ -59,8 +59,8 @@ robot_ee_to_joints_processor = RobotProcessorPipeline(
             initial_guess_current_joints=False,  # Because replay is open loop
         ),
     ],
-    to_transition=action_to_transition,
+    to_transition=robot_action_to_transition,
-    to_output=transition_to_action,
+    to_output=transition_to_robot_action,
 )
 
 robot_ee_to_joints_processor.reset()

examples/phone_to_so100/teleoperate.py

@@ -17,7 +17,7 @@ import time
 
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import RobotProcessorPipeline
-from lerobot.processor.converters import action_to_transition, transition_to_action
+from lerobot.processor.converters import robot_action_to_transition, transition_to_robot_action
 from lerobot.robots.so100_follower.config_so100_follower import SO100FollowerConfig
 from lerobot.robots.so100_follower.robot_kinematic_processor import (
     AddRobotObservationAsComplimentaryData,
@@ -72,8 +72,8 @@ phone_to_robot_joints_processor = RobotProcessorPipeline(
             speed_factor=20.0,
         ),
     ],
-    to_transition=action_to_transition,
+    to_transition=robot_action_to_transition,
-    to_output=transition_to_action,
+    to_output=transition_to_robot_action,
 )
 
 robot.connect()

src/lerobot/processor/__init__.py

@@ -46,11 +46,13 @@ from .pipeline import (
     IdentityProcessorStep,
     InfoProcessorStep,
     ObservationProcessorStep,
+    PolicyActionProcessorStep,
     PolicyProcessorPipeline,
     ProcessorKwargs,
     ProcessorStep,
     ProcessorStepRegistry,
     RewardProcessorStep,
+    RobotActionProcessorStep,
     RobotProcessorPipeline,
     TruncatedProcessorStep,
 )
@@ -81,10 +83,12 @@ __all__ = [
     "NormalizerProcessorStep",
     "Numpy2TorchActionProcessorStep",
     "ObservationProcessorStep",
+    "PolicyActionProcessorStep",
     "PolicyProcessorPipeline",
     "ProcessorKwargs",
     "ProcessorStep",
     "ProcessorStepRegistry",
+    "RobotActionProcessorStep",
     "RenameObservationsProcessorStep",
     "RewardClassifierProcessorStep",
     "RewardProcessorStep",

src/lerobot/processor/batch_processor.py

@@ -27,11 +27,11 @@ from torch import Tensor
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.constants import OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 
-from .core import EnvTransition
+from .core import EnvTransition, PolicyAction
 from .pipeline import (
-    ActionProcessorStep,
     ComplementaryDataProcessorStep,
     ObservationProcessorStep,
+    PolicyActionProcessorStep,
     ProcessorStep,
     ProcessorStepRegistry,
 )
@@ -39,14 +39,14 @@ from .pipeline import (
 
 @dataclass
 @ProcessorStepRegistry.register(name="to_batch_processor_action")
-class AddBatchDimensionActionStep(ActionProcessorStep):
+class AddBatchDimensionActionStep(PolicyActionProcessorStep):
     """
     Processor step to add a batch dimension to a 1D tensor action.
 
     This is useful for creating a batch of size 1 from a single action sample.
     """
 
-    def action(self, action: Tensor) -> Tensor:
+    def action(self, action: PolicyAction) -> PolicyAction:
         """
         Adds a batch dimension to the action if it's a 1D tensor.
 
@@ -56,7 +56,7 @@ class AddBatchDimensionActionStep(ActionProcessorStep):
         Returns:
             The action tensor with an added batch dimension.
         """
-        if not isinstance(action, Tensor) or action.dim() != 1:
+        if action.dim() != 1:
             return action
         return action.unsqueeze(0)
 

src/lerobot/processor/converters.py

@@ -26,7 +26,7 @@ import torch
 
 from lerobot.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, REWARD, TRUNCATED
 
-from .core import EnvTransition, TransitionKey
+from .core import EnvTransition, PolicyAction, RobotAction, TransitionKey
 
 
 @singledispatch
@@ -243,7 +243,7 @@ def _merge_transitions(base: EnvTransition, other: EnvTransition) -> EnvTransiti
 
 def create_transition(
     observation: dict[str, Any] | None = None,
-    action: dict[str, Any] | None = None,
+    action: PolicyAction | RobotAction | None = None,
     reward: float = 0.0,
     done: bool = False,
     truncated: bool = False,
@@ -276,9 +276,9 @@ def create_transition(
     }
 
 
-def action_to_transition(action: dict[str, Any]) -> EnvTransition:
+def robot_action_to_transition(action: RobotAction) -> EnvTransition:
     """
-    Convert a raw action dictionary into a standardized `EnvTransition`.
+    Convert a raw robot action dictionary into a standardized `EnvTransition`.
 
     The keys in the action dictionary are prefixed with "action." and stored under
     the `ACTION` key in the transition. Values are converted to tensors, except for
@@ -315,9 +315,9 @@ def observation_to_transition(observation: dict[str, Any]) -> EnvTransition:
     return create_transition(observation={**state, **image_observations}, action={})
 
 
-def transition_to_action(transition: EnvTransition) -> dict[str, Any]:
+def transition_to_robot_action(transition: EnvTransition) -> RobotAction:
     """
-    Extract a raw action dictionary for a robot from an `EnvTransition`.
+    Extract a raw robot action dictionary for a robot from an `EnvTransition`.
 
     This function searches for keys in the format "action.*.pos" or "action.*.vel"
     and converts them into a flat dictionary suitable for sending to a robot controller.
@@ -460,6 +460,10 @@ def batch_to_transition(batch: dict[str, Any]) -> EnvTransition:
     if not isinstance(batch, dict):
         raise ValueError(f"EnvTransition must be a dictionary. Got {type(batch).__name__}")
 
+    action = batch.get("action")
+    if action is not None and not isinstance(action, PolicyAction):
+        raise ValueError(f"Action should be a PolicyAction type got {type(action)}")
+
     # Extract observation and complementary data keys.
     observation_keys = {k: v for k, v in batch.items() if k.startswith("observation.")}
     complementary_data = _extract_complementary_data(batch)

src/lerobot/processor/core.py

@@ -17,8 +17,9 @@
 from __future__ import annotations
 
 from enum import Enum
-from typing import Any, TypedDict
+from typing import Any, TypeAlias, TypedDict
 
+import numpy as np
 import torch
 
 
@@ -35,11 +36,16 @@ class TransitionKey(str, Enum):
     COMPLEMENTARY_DATA = "complementary_data"
 
 
+PolicyAction: TypeAlias = torch.Tensor
+RobotAction: TypeAlias = dict[str, Any]
+EnvAction: TypeAlias = np.ndarray
+
+
 EnvTransition = TypedDict(
     "EnvTransition",
     {
         TransitionKey.OBSERVATION.value: dict[str, Any] | None,
-        TransitionKey.ACTION.value: Any | torch.Tensor | None,
+        TransitionKey.ACTION.value: PolicyAction | RobotAction | EnvAction | None,
         TransitionKey.REWARD.value: float | torch.Tensor | None,
         TransitionKey.DONE.value: bool | torch.Tensor | None,
         TransitionKey.TRUNCATED.value: bool | torch.Tensor | None,

src/lerobot/processor/delta_action_processor.py

@@ -16,11 +16,10 @@
 
 from dataclasses import dataclass
 
-from torch import Tensor
-
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
 
-from .pipeline import ActionProcessorStep, ProcessorStepRegistry
+from .core import PolicyAction, RobotAction
+from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
 
 
 @ProcessorStepRegistry.register("map_tensor_to_delta_action_dict")
@@ -40,7 +39,10 @@ class MapTensorToDeltaActionDictStep(ActionProcessorStep):
 
     use_gripper: bool = True
 
-    def action(self, action: Tensor) -> dict:
+    def action(self, action: PolicyAction) -> RobotAction:
+        if not isinstance(action, PolicyAction):
+            raise ValueError("Only PolicyAction is supported for this processor")
+
         if action.dim() > 1:
             action = action.squeeze(0)
 
@@ -69,7 +71,7 @@ class MapTensorToDeltaActionDictStep(ActionProcessorStep):
 
 @ProcessorStepRegistry.register("map_delta_action_to_robot_action")
 @dataclass
-class MapDeltaActionToRobotActionStep(ActionProcessorStep):
+class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
     """
     Maps delta actions from teleoperators to robot target actions for inverse kinematics.
 
@@ -89,7 +91,7 @@ class MapDeltaActionToRobotActionStep(ActionProcessorStep):
     rotation_scale: float = 0.0  # No rotation deltas for gamepad/keyboard
     noise_threshold: float = 1e-3  # 1 mm threshold to filter out noise
 
-    def action(self, action: dict) -> dict:
+    def action(self, action: RobotAction) -> RobotAction:
         # NOTE (maractingi): Action can be a dict from the teleop_devices or a tensor from the policy
         # TODO (maractingi): changing this target_xyz naming convention from the teleop_devices
         delta_x = action.pop("delta_x", 0.0)

src/lerobot/processor/device_processor.py

@@ -27,7 +27,7 @@ import torch
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.utils.utils import get_safe_torch_device
 
-from .core import EnvTransition, TransitionKey
+from .core import EnvTransition, PolicyAction, TransitionKey
 from .pipeline import ProcessorStep, ProcessorStepRegistry
 
 
@@ -129,6 +129,10 @@ class DeviceProcessorStep(ProcessorStep):
             A new `EnvTransition` object with all tensors moved to the target device and dtype.
         """
         new_transition = transition.copy()
+        action = new_transition.get(TransitionKey.ACTION)
+
+        if action is not None and not isinstance(action, PolicyAction):
+            raise ValueError(f"If action is not None should be a PolicyAction type got {type(action)}")
 
         simple_tensor_keys = [
             TransitionKey.ACTION,

src/lerobot/processor/gym_action_processor.py

@@ -16,12 +16,10 @@
 
 from dataclasses import dataclass
 
-import numpy as np
-import torch
-
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 
 from .converters import to_tensor
+from .core import EnvAction, PolicyAction
 from .pipeline import ActionProcessorStep, ProcessorStepRegistry
 
 
@@ -42,10 +40,10 @@ class Torch2NumpyActionProcessorStep(ActionProcessorStep):
 
     squeeze_batch_dim: bool = True
 
-    def action(self, action: torch.Tensor) -> np.ndarray:
+    def action(self, action: PolicyAction) -> EnvAction:
-        if not isinstance(action, torch.Tensor):
+        if not isinstance(action, PolicyAction):
             raise TypeError(
-                f"Expected torch.Tensor or None, got {type(action).__name__}. "
+                f"Expected PolicyAction or None, got {type(action).__name__}. "
                 "Use appropriate processor for non-tensor actions."
             )
 
@@ -80,8 +78,8 @@ class Numpy2TorchActionProcessorStep(ActionProcessorStep):
     by a policy or model.
     """
 
-    def action(self, action: np.ndarray) -> torch.Tensor:
+    def action(self, action: EnvAction) -> PolicyAction:
-        if not isinstance(action, np.ndarray):
+        if not isinstance(action, EnvAction):
             raise TypeError(
                 f"Expected np.ndarray or None, got {type(action).__name__}. "
                 "Use appropriate processor for non-tensor actions."

src/lerobot/processor/hil_processor.py

@@ -28,7 +28,7 @@ from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.teleoperators.teleoperator import Teleoperator
 from lerobot.teleoperators.utils import TeleopEvents
 
-from .core import EnvTransition, TransitionKey
+from .core import EnvTransition, PolicyAction, TransitionKey
 from .pipeline import (
     ComplementaryDataProcessorStep,
     InfoProcessorStep,
@@ -416,8 +416,8 @@ class InterventionActionProcessorStep(ProcessorStep):
             reward, and termination status.
         """
         action = transition.get(TransitionKey.ACTION)
-        if action is None:
+        if not isinstance(action, PolicyAction):
-            return transition
+            raise ValueError(f"Action should be a PolicyAction type got {type(action)}")
 
         # Get intervention signals from complementary data
         info = transition.get(TransitionKey.INFO, {})

src/lerobot/processor/normalize_processor.py

@@ -28,7 +28,7 @@ from lerobot.configs.types import FeatureType, NormalizationMode, PipelineFeatur
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
 from .converters import from_tensor_to_numpy, to_tensor
-from .core import EnvTransition, TransitionKey
+from .core import EnvTransition, PolicyAction, TransitionKey
 from .pipeline import PolicyProcessorPipeline, ProcessorStep, ProcessorStepRegistry
 
 
@@ -345,7 +345,13 @@ class NormalizerProcessorStep(_NormalizationMixin, ProcessorStep):
 
         # Handle action normalization.
         action = new_transition.get(TransitionKey.ACTION)
-        if action is not None:
+
+        if action is None:
+            return new_transition
+
+        if not isinstance(action, PolicyAction):
+            raise ValueError(f"Action should be a PolicyAction type got {type(action)}")
+
         new_transition[TransitionKey.ACTION] = self._normalize_action(action, inverse=False)
 
         return new_transition
@@ -401,7 +407,12 @@ class UnnormalizerProcessorStep(_NormalizationMixin, ProcessorStep):
 
         # Handle action unnormalization.
         action = new_transition.get(TransitionKey.ACTION)
-        if action is not None:
+
+        if action is None:
+            return new_transition
+        if not isinstance(action, PolicyAction):
+            raise ValueError(f"Action should be a PolicyAction type got {type(action)}")
+
         new_transition[TransitionKey.ACTION] = self._normalize_action(action, inverse=True)
 
         return new_transition

src/lerobot/processor/pipeline.py

@@ -32,7 +32,7 @@ from safetensors.torch import load_file, save_file
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 
 from .converters import batch_to_transition, create_transition, transition_to_batch
-from .core import EnvTransition, TransitionKey
+from .core import EnvAction, EnvTransition, PolicyAction, RobotAction, TransitionKey
 
 # Type variable for generic processor output type
 TOutput = TypeVar("TOutput")
@@ -859,7 +859,9 @@ class ActionProcessorStep(ProcessorStep, ABC):
     """
 
     @abstractmethod
-    def action(self, action) -> Any | torch.Tensor:
+    def action(
+        self, action: PolicyAction | RobotAction | EnvAction
+    ) -> PolicyAction | RobotAction | EnvAction:
         """Process the action component.
 
         Args:
@@ -878,6 +880,82 @@ class ActionProcessorStep(ProcessorStep, ABC):
         if action is None:
             raise ValueError("ActionProcessorStep requires an action in the transition.")
 
+        processed_action = self.action(action)
+        new_transition[TransitionKey.ACTION] = processed_action
+        raise ValueError("ActionProcessorStep requires an action in the transition.")
+
+
+class RobotActionProcessorStep(ProcessorStep, ABC):
+    """Base class for processors that modify only the robot action component of a transition.
+
+    Subclasses should override the `action` method to implement custom robot action processing.
+    This class handles the boilerplate of extracting and reinserting the processed action
+    into the transition dict, eliminating the need to implement the `__call__` method in subclasses.
+
+
+    By inheriting from this class, you avoid writing repetitive code to handle transition dict
+    manipulation, focusing only on the specific robot action processing logic.
+    """
+
+    @abstractmethod
+    def action(self, action: RobotAction) -> RobotAction:
+        """Process the robot action component.
+
+        Args:
+            action: The robot action to process
+
+        Returns:
+            The processed robot action
+        """
+        ...
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        self._current_transition = transition.copy()
+        new_transition = self._current_transition
+
+        action = new_transition.get(TransitionKey.ACTION)
+        # NOTE: We can't use isinstance(action, RobotAction) because RobotAction is a dict[str, Any]
+        # because Any is generic
+        if not isinstance(action, dict):
+            raise ValueError(f"Action should be a RobotAction type got {type(action)}")
+
+        processed_action = self.action(action=action)
+        new_transition[TransitionKey.ACTION] = processed_action
+        return new_transition
+
+
+class PolicyActionProcessorStep(ProcessorStep, ABC):
+    """Base class for processors that modify only the policy action component of a transition.
+
+    Subclasses should override the `action` method to implement custom policy action processing.
+    This class handles the boilerplate of extracting and reinserting the processed action
+    into the transition dict, eliminating the need to implement the `__call__` method in subclasses.
+
+
+    By inheriting from this class, you avoid writing repetitive code to handle transition dict
+    manipulation, focusing only on the specific policy action processing logic.
+    """
+
+    @abstractmethod
+    def action(self, action: PolicyAction) -> PolicyAction:
+        """Process the policy action component.
+
+        Args:
+            action: The policy action to process
+
+        Returns:
+            The processed policy action
+        """
+        ...
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        self._current_transition = transition.copy()
+        new_transition = self._current_transition
+
+        action = new_transition.get(TransitionKey.ACTION)
+        if not isinstance(action, PolicyAction):
+            raise ValueError(f"Action should be a PolicyAction type got {type(action)}")
+
         processed_action = self.action(action)
         new_transition[TransitionKey.ACTION] = processed_action
         return new_transition

src/lerobot/record.py

@@ -85,11 +85,11 @@ from lerobot.processor import (
     TransitionKey,
 )
 from lerobot.processor.converters import (
-    action_to_transition,
     identity_transition,
     observation_to_transition,
-    transition_to_action,
+    robot_action_to_transition,
     transition_to_dataset_frame,
+    transition_to_robot_action,
 )
 from lerobot.processor.rename_processor import rename_stats
 from lerobot.robots import (  # noqa: F401
@@ -255,7 +255,9 @@ def record_loop(
     teleop_action_processor: RobotProcessorPipeline[EnvTransition] = (
         teleop_action_processor
         or RobotProcessorPipeline(
-            steps=[IdentityProcessorStep()], to_transition=action_to_transition, to_output=identity_transition
+            steps=[IdentityProcessorStep()],
+            to_transition=robot_action_to_transition,
+            to_output=identity_transition,
         )
     )
     robot_action_processor: RobotProcessorPipeline[dict[str, Any]] = (
@@ -263,7 +265,7 @@ def record_loop(
         or RobotProcessorPipeline(
             steps=[IdentityProcessorStep()],
             to_transition=identity_transition,
-            to_output=transition_to_action,
+            to_output=transition_to_robot_action,
         )
     )
     robot_observation_processor: RobotProcessorPipeline[EnvTransition] = (

src/lerobot/replay.py

@@ -48,7 +48,7 @@ from pprint import pformat
 from lerobot.configs import parser
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.processor import IdentityProcessorStep, RobotProcessorPipeline
-from lerobot.processor.converters import action_to_transition, transition_to_action
+from lerobot.processor.converters import robot_action_to_transition, transition_to_robot_action
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
@@ -97,8 +97,8 @@ def replay(cfg: ReplayConfig):
     # Initialize robot action processor with default if not provided
     robot_action_processor = cfg.robot_action_processor or RobotProcessorPipeline(
         steps=[IdentityProcessorStep()],
-        to_transition=action_to_transition,
+        to_transition=robot_action_to_transition,
-        to_output=transition_to_action,  # type: ignore[arg-type]
+        to_output=transition_to_robot_action,  # type: ignore[arg-type]
     )
 
     # Reset processor

src/lerobot/robots/so100_follower/robot_kinematic_processor.py

@@ -22,21 +22,22 @@ from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeatur
 from lerobot.constants import OBS_STATE
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.processor import (
-    ActionProcessorStep,
     ComplementaryDataProcessorStep,
     EnvTransition,
     ObservationProcessorStep,
     ProcessorStep,
     ProcessorStepRegistry,
+    RobotActionProcessorStep,
     TransitionKey,
 )
+from lerobot.processor.core import RobotAction
 from lerobot.robots.robot import Robot
 from lerobot.utils.rotation import Rotation
 
 
 @ProcessorStepRegistry.register("ee_reference_and_delta")
 @dataclass
-class EEReferenceAndDelta(ActionProcessorStep):
+class EEReferenceAndDelta(RobotActionProcessorStep):
     """
     Computes a target end-effector pose from a relative delta command.
 
@@ -72,7 +73,7 @@ class EEReferenceAndDelta(ActionProcessorStep):
     _prev_enabled: bool = field(default=False, init=False, repr=False)
     _command_when_disabled: np.ndarray | None = field(default=None, init=False, repr=False)
 
-    def action(self, action):
+    def action(self, action: RobotAction) -> RobotAction:
         new_action = action.copy()
         comp = self.transition.get(TransitionKey.COMPLEMENTARY_DATA)
 
@@ -171,7 +172,7 @@ class EEReferenceAndDelta(ActionProcessorStep):
 
 @ProcessorStepRegistry.register("ee_bounds_and_safety")
 @dataclass
-class EEBoundsAndSafety(ActionProcessorStep):
+class EEBoundsAndSafety(RobotActionProcessorStep):
     """
     Clips the end-effector pose to predefined bounds and checks for unsafe jumps.
 
@@ -192,7 +193,7 @@ class EEBoundsAndSafety(ActionProcessorStep):
     _last_pos: np.ndarray | None = field(default=None, init=False, repr=False)
     _last_twist: np.ndarray | None = field(default=None, init=False, repr=False)
 
-    def action(self, act: dict) -> dict:
+    def action(self, act: RobotAction) -> RobotAction:
         x = act.get("ee.x", None)
         y = act.get("ee.y", None)
         z = act.get("ee.z", None)
@@ -266,6 +267,10 @@ class InverseKinematicsEEToJoints(ProcessorStep):
     def __call__(self, transition: EnvTransition) -> EnvTransition:
         new_transition = transition.copy()
         act = new_transition.get(TransitionKey.ACTION) or {}
+
+        if not isinstance(act, dict):
+            raise ValueError(f"Action should be a RobotAction type got {type(act)}")
+
         comp = new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
 
         x = act.get("ee.x", None)
@@ -361,6 +366,9 @@ class GripperVelocityToJoint(ProcessorStep):
         act = new_transition.get(TransitionKey.ACTION) or {}
         comp = new_transition.get(TransitionKey.COMPLEMENTARY_DATA) or {}
 
+        if not isinstance(act, dict):
+            raise ValueError(f"Action should be a RobotAction type got {type(act)}")
+
         if "gripper" not in act:
             raise ValueError("Required action key 'gripper' not found in transition")
 

src/lerobot/teleoperate.py

@@ -64,10 +64,10 @@ from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraCon
 from lerobot.configs import parser
 from lerobot.processor import EnvTransition, IdentityProcessorStep, RobotProcessorPipeline, TransitionKey
 from lerobot.processor.converters import (
-    action_to_transition,
     identity_transition,
     observation_to_transition,
-    transition_to_action,
+    robot_action_to_transition,
+    transition_to_robot_action,
 )
 from lerobot.robots import (  # noqa: F401
     Robot,
@@ -140,7 +140,9 @@ def teleop_loop(
     teleop_action_processor: RobotProcessorPipeline[EnvTransition] = (
         teleop_action_processor
         or RobotProcessorPipeline(
-            steps=[IdentityProcessorStep()], to_transition=action_to_transition, to_output=identity_transition
+            steps=[IdentityProcessorStep()],
+            to_transition=robot_action_to_transition,
+            to_output=identity_transition,
         )
     )
     robot_action_processor: RobotProcessorPipeline[dict[str, Any]] = (
@@ -148,7 +150,7 @@ def teleop_loop(
         or RobotProcessorPipeline(
             steps=[IdentityProcessorStep()],
             to_transition=identity_transition,
-            to_output=transition_to_action,  # type: ignore[arg-type]
+            to_output=transition_to_robot_action,  # type: ignore[arg-type]
         )
     )
     robot_observation_processor: RobotProcessorPipeline[EnvTransition] = (

tests/processor/test_batch_conversion.py

@@ -49,7 +49,7 @@ def test_batch_to_transition_observation_grouping():
         "observation.image.top": torch.randn(1, 3, 128, 128),
         "observation.image.left": torch.randn(1, 3, 128, 128),
         "observation.state": [1, 2, 3, 4],
-        "action": "action_data",
+        "action": torch.tensor([0.1, 0.2, 0.3, 0.4]),
         "next.reward": 1.5,
         "next.done": True,
         "next.truncated": False,
@@ -74,7 +74,7 @@ def test_batch_to_transition_observation_grouping():
     assert transition[TransitionKey.OBSERVATION]["observation.state"] == [1, 2, 3, 4]
 
     # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.1, 0.2, 0.3, 0.4]))
     assert transition[TransitionKey.REWARD] == 1.5
     assert transition[TransitionKey.DONE]
     assert not transition[TransitionKey.TRUNCATED]
@@ -123,7 +123,7 @@ def test_transition_to_batch_observation_flattening():
 def test_no_observation_keys():
     """Test behavior when there are no observation.* keys."""
     batch = {
-        "action": "action_data",
+        "action": torch.tensor([1.0, 2.0]),
         "next.reward": 2.0,
         "next.done": False,
         "next.truncated": True,
@@ -136,7 +136,7 @@ def test_no_observation_keys():
     assert transition[TransitionKey.OBSERVATION] is None
 
     # Check other fields
-    assert transition[TransitionKey.ACTION] == "action_data"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([1.0, 2.0]))
     assert transition[TransitionKey.REWARD] == 2.0
     assert not transition[TransitionKey.DONE]
     assert transition[TransitionKey.TRUNCATED]
@@ -144,7 +144,7 @@ def test_no_observation_keys():
 
     # Round trip should work
     reconstructed_batch = transition_to_batch(transition)
-    assert reconstructed_batch["action"] == "action_data"
+    assert torch.allclose(reconstructed_batch["action"], torch.tensor([1.0, 2.0]))
     assert reconstructed_batch["next.reward"] == 2.0
     assert not reconstructed_batch["next.done"]
     assert reconstructed_batch["next.truncated"]
@@ -153,13 +153,13 @@ def test_no_observation_keys():
 
 def test_minimal_batch():
     """Test with minimal batch containing only observation.* and action."""
-    batch = {"observation.state": "minimal_state", "action": "minimal_action"}
+    batch = {"observation.state": "minimal_state", "action": torch.tensor([0.5])}
 
     transition = batch_to_transition(batch)
 
     # Check observation
     assert transition[TransitionKey.OBSERVATION] == {"observation.state": "minimal_state"}
-    assert transition[TransitionKey.ACTION] == "minimal_action"
+    assert torch.allclose(transition[TransitionKey.ACTION], torch.tensor([0.5]))
 
     # Check defaults
     assert transition[TransitionKey.REWARD] == 0.0
@@ -171,7 +171,7 @@ def test_minimal_batch():
     # Round trip
     reconstructed_batch = transition_to_batch(transition)
     assert reconstructed_batch["observation.state"] == "minimal_state"
-    assert reconstructed_batch["action"] == "minimal_action"
+    assert torch.allclose(reconstructed_batch["action"], torch.tensor([0.5]))
     assert reconstructed_batch["next.reward"] == 0.0
     assert not reconstructed_batch["next.done"]
     assert not reconstructed_batch["next.truncated"]

tests/processor/test_batch_processor.py

@@ -38,7 +38,7 @@ def test_state_1d_to_2d():
     # Test observation.state
     state_1d = torch.randn(7)
     observation = {OBS_STATE: state_1d}
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -54,7 +54,7 @@ def test_env_state_1d_to_2d():
     # Test observation.environment_state
     env_state_1d = torch.randn(10)
     observation = {OBS_ENV_STATE: env_state_1d}
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -70,7 +70,7 @@ def test_image_3d_to_4d():
     # Test observation.image
     image_3d = torch.randn(224, 224, 3)
     observation = {OBS_IMAGE: image_3d}
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -90,7 +90,7 @@ def test_multiple_images_3d_to_4d():
         f"{OBS_IMAGES}.camera1": image1_3d,
         f"{OBS_IMAGES}.camera2": image2_3d,
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -118,7 +118,7 @@ def test_already_batched_tensors_unchanged():
         OBS_ENV_STATE: env_state_2d,
         OBS_IMAGE: image_4d,
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -142,7 +142,7 @@ def test_higher_dimensional_tensors_unchanged():
         OBS_STATE: state_3d,
         OBS_IMAGE: image_5d,
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -163,7 +163,7 @@ def test_non_tensor_values_unchanged():
         "custom_key": 42,  # Integer
         "another_key": {"nested": "dict"},  # Dict
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -180,7 +180,7 @@ def test_none_observation():
     """Test processor handles None observation gracefully."""
     processor = AddBatchDimensionProcessorStep()
 
-    transition = create_transition(observation={}, action={})
+    transition = create_transition(observation={}, action=torch.empty(0))
     result = processor(transition)
 
     assert result[TransitionKey.OBSERVATION] == {}
@@ -191,7 +191,7 @@ def test_empty_observation():
     processor = AddBatchDimensionProcessorStep()
 
     observation = {}
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
 
@@ -216,7 +216,7 @@ def test_mixed_observation():
         "other_tensor": other_tensor,
         "non_tensor": "string_value",
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
     processed_obs = result[TransitionKey.OBSERVATION]
@@ -243,7 +243,7 @@ def test_integration_with_robot_processor():
         OBS_STATE: torch.randn(7),
         OBS_IMAGE: torch.randn(224, 224, 3),
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = pipeline(transition)
     processed_obs = result[TransitionKey.OBSERVATION]
@@ -299,7 +299,7 @@ def test_save_and_load_pretrained():
 
         # Test functionality of loaded processor
         observation = {OBS_STATE: torch.randn(5)}
-        transition = create_transition(observation=observation, action={})
+        transition = create_transition(observation=observation, action=torch.empty(0))
 
         result = loaded_pipeline(transition)
         assert result[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 5)
@@ -333,7 +333,7 @@ def test_registry_based_save_load():
             OBS_STATE: torch.randn(3),
             OBS_IMAGE: torch.randn(100, 100, 3),
         }
-        transition = create_transition(observation=observation, action={})
+        transition = create_transition(observation=observation, action=torch.empty(0))
 
         result = loaded_pipeline(transition)
         processed_obs = result[TransitionKey.OBSERVATION]
@@ -355,7 +355,7 @@ def test_device_compatibility():
         OBS_STATE: state_1d,
         OBS_IMAGE: image_3d,
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
     processed_obs = result[TransitionKey.OBSERVATION]
@@ -415,7 +415,7 @@ def test_edge_case_zero_dimensional_tensors():
         OBS_STATE: scalar_tensor,
         "scalar_value": scalar_tensor,
     }
-    transition = create_transition(observation=observation, action={})
+    transition = create_transition(observation=observation, action=torch.empty(0))
 
     result = processor(transition)
     processed_obs = result[TransitionKey.OBSERVATION]
@@ -490,42 +490,43 @@ def test_action_scalar_tensor():
     assert torch.equal(result[TransitionKey.ACTION], action_scalar)
 
 
-def test_action_non_tensor():
+def test_action_non_tensor_raises_error():
-    """Test that non-tensor actions remain unchanged."""
+    """Test that non-tensor actions raise ValueError for PolicyAction processors."""
     processor = AddBatchDimensionProcessorStep()
 
-    # List action
+    # List action should raise error
     action_list = [0.1, 0.2, 0.3, 0.4]
-    transition = create_transition(action=action_list, observation={})
+    transition = create_transition(action=action_list)
-    result = processor(transition)
+    with pytest.raises(ValueError, match="Action should be a PolicyAction type"):
-    assert result[TransitionKey.ACTION] == action_list
+        processor(transition)
 
-    # Numpy array action (as Python object, not converted)
+    # Numpy array action should raise error
     action_numpy = np.array([1, 2, 3, 4])
-    transition = create_transition(action=action_numpy, observation={})
+    transition = create_transition(action=action_numpy)
-    result = processor(transition)
+    with pytest.raises(ValueError, match="Action should be a PolicyAction type"):
-    assert np.array_equal(result[TransitionKey.ACTION], action_numpy)
+        processor(transition)
 
-    # String action (edge case)
+    # String action should raise error
     action_string = "forward"
-    transition = create_transition(action=action_string, observation={})
+    transition = create_transition(action=action_string)
-    result = processor(transition)
+    with pytest.raises(ValueError, match="Action should be a PolicyAction type"):
-    assert result[TransitionKey.ACTION] == action_string
+        processor(transition)
 
-    # Dict action (structured action)
+    # Dict action should raise error
     action_dict = {"linear": [0.5, 0.0], "angular": 0.2}
-    transition = create_transition(action=action_dict, observation={})
+    transition = create_transition(action=action_dict)
-    result = processor(transition)
+    with pytest.raises(ValueError, match="Action should be a PolicyAction type"):
-    assert result[TransitionKey.ACTION] == action_dict
+        processor(transition)
 
 
 def test_action_none():
-    """Test that None action is handled correctly."""
+    """Test that empty action tensor is handled correctly."""
     processor = AddBatchDimensionProcessorStep()
 
-    transition = create_transition(action={}, observation={})
+    transition = create_transition(action=torch.empty(0), observation={})
     result = processor(transition)
-    assert result[TransitionKey.ACTION] == {}
+    # Empty 1D tensor becomes empty 2D tensor with batch dimension
+    assert result[TransitionKey.ACTION].shape == (1, 0)
 
 
 def test_action_with_observation():
@@ -630,7 +631,9 @@ def test_task_string_to_list():
 
     # Create complementary data with string task
     complementary_data = {"task": "pick_cube"}
-    transition = create_transition(action={}, observation={}, complementary_data=complementary_data)
+    transition = create_transition(
+        action=torch.empty(0), observation={}, complementary_data=complementary_data
+    )
 
     result = processor(transition)
 
@@ -647,14 +650,18 @@ def test_task_string_validation():
 
     # Valid string task - should be converted to list
     complementary_data = {"task": "valid_task"}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
     assert processed_comp_data["task"] == ["valid_task"]
 
     # Valid list of strings - should remain unchanged
     complementary_data = {"task": ["task1", "task2"]}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
     assert processed_comp_data["task"] == ["task1", "task2"]
@@ -676,7 +683,9 @@ def test_task_list_of_strings():
 
     for task_list in test_lists:
         complementary_data = {"task": task_list}
-        transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+        transition = create_transition(
+            complementary_data=complementary_data, observation={}, action=torch.empty(0)
+        )
 
         result = processor(transition)
 
@@ -690,7 +699,7 @@ def test_complementary_data_none():
     """Test processor handles None complementary_data gracefully."""
     processor = AddBatchDimensionProcessorStep()
 
-    transition = create_transition(complementary_data=None, action={}, observation={})
+    transition = create_transition(complementary_data=None, action=torch.empty(0), observation={})
     result = processor(transition)
 
     assert result[TransitionKey.COMPLEMENTARY_DATA] == {}
@@ -701,7 +710,9 @@ def test_complementary_data_empty():
     processor = AddBatchDimensionProcessorStep()
 
     complementary_data = {}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -717,7 +728,9 @@ def test_complementary_data_no_task():
         "timestamp": 1234567890.0,
         "extra_info": "some data",
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -736,7 +749,9 @@ def test_complementary_data_mixed():
         "difficulty": "hard",
         "metadata": {"scene": "kitchen"},
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -803,7 +818,9 @@ def test_task_comprehensive_string_cases():
     # Test that all string tasks get properly batched
     for task in string_tasks:
         complementary_data = {"task": task}
-        transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+        transition = create_transition(
+            complementary_data=complementary_data, observation={}, action=torch.empty(0)
+        )
 
         result = processor(transition)
 
@@ -825,7 +842,9 @@ def test_task_comprehensive_string_cases():
 
     for task_list in list_tasks:
         complementary_data = {"task": task_list}
-        transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+        transition = create_transition(
+            complementary_data=complementary_data, observation={}, action=torch.empty(0)
+        )
 
         result = processor(transition)
 
@@ -845,7 +864,9 @@ def test_task_preserves_other_keys():
         "config": {"speed": "slow", "precision": "high"},
         "metrics": [1.0, 2.0, 3.0],
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -869,7 +890,9 @@ def test_index_scalar_to_1d():
     # Create 0D index tensor (scalar)
     index_0d = torch.tensor(42, dtype=torch.int64)
     complementary_data = {"index": index_0d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -886,7 +909,9 @@ def test_task_index_scalar_to_1d():
     # Create 0D task_index tensor (scalar)
     task_index_0d = torch.tensor(7, dtype=torch.int64)
     complementary_data = {"task_index": task_index_0d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -908,7 +933,9 @@ def test_index_and_task_index_together():
         "task_index": task_index_0d,
         "task": "pick_object",
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -936,13 +963,17 @@ def test_index_already_batched():
 
     # Test 1D (already batched)
     complementary_data = {"index": index_1d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["index"], index_1d)
 
     # Test 2D
     complementary_data = {"index": index_2d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["index"], index_2d)
 
@@ -957,13 +988,17 @@ def test_task_index_already_batched():
 
     # Test 1D (already batched)
     complementary_data = {"task_index": task_index_1d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["task_index"], task_index_1d)
 
     # Test 2D
     complementary_data = {"task_index": task_index_2d}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
     result = processor(transition)
     assert torch.equal(result[TransitionKey.COMPLEMENTARY_DATA]["task_index"], task_index_2d)
 
@@ -976,7 +1011,9 @@ def test_index_non_tensor_unchanged():
         "index": 42,  # Plain int, not tensor
         "task_index": [1, 2, 3],  # List, not tensor
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -999,7 +1036,9 @@ def test_index_dtype_preservation():
             "index": index_0d,
             "task_index": task_index_0d,
         }
-        transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+        transition = create_transition(
+            complementary_data=complementary_data, observation={}, action=torch.empty(0)
+        )
 
         result = processor(transition)
 
@@ -1062,7 +1101,9 @@ def test_index_device_compatibility():
         "index": index_0d,
         "task_index": task_index_0d,
     }
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
     processed_comp_data = result[TransitionKey.COMPLEMENTARY_DATA]
@@ -1081,7 +1122,9 @@ def test_empty_index_tensor():
     # Empty 0D tensor doesn't make sense, but test empty 1D
     index_empty = torch.tensor([], dtype=torch.int64)
     complementary_data = {"index": index_empty}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     result = processor(transition)
 
@@ -1116,7 +1159,9 @@ def test_task_processing_creates_new_transition():
     processor = AddBatchDimensionProcessorStep()
 
     complementary_data = {"task": "sort_objects"}
-    transition = create_transition(complementary_data=complementary_data, observation={}, action={})
+    transition = create_transition(
+        complementary_data=complementary_data, observation={}, action=torch.empty(0)
+    )
 
     # Store reference to original transition and complementary_data
     original_transition = transition

tests/processor/test_normalize_processor.py

@@ -329,14 +329,14 @@ def test_min_max_unnormalization(action_stats_min_max):
     assert torch.allclose(unnormalized_action, expected)
 
 
-def test_numpy_action_input(action_stats_mean_std):
+def test_tensor_action_input(action_stats_mean_std):
     features = _create_action_features()
     norm_map = _create_action_norm_map_mean_std()
     unnormalizer = UnnormalizerProcessorStep(
         features=features, norm_map=norm_map, stats={"action": action_stats_mean_std}
     )
 
-    normalized_action = np.array([1.0, -0.5, 2.0], dtype=np.float32)
+    normalized_action = torch.tensor([1.0, -0.5, 2.0], dtype=torch.float32)
     transition = create_transition(action=normalized_action)
 
     unnormalized_transition = unnormalizer(transition)

tests/processor/test_sac_processor.py

@@ -371,12 +371,12 @@ def test_sac_processor_edge_cases():
     assert processed[TransitionKey.OBSERVATION] == {}
     assert processed[TransitionKey.ACTION].shape == (1, 5)
 
-    # Test with None action
+    # Test with zero action (representing "null" action)
-    transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action={})
+    transition = create_transition(observation={OBS_STATE: torch.randn(10)}, action=torch.zeros(5))
     processed = preprocessor(transition)
     assert processed[TransitionKey.OBSERVATION][OBS_STATE].shape == (1, 10)
-    # When action is None, it may still be present with None value
+    # Action should be present and batched, even if it's zeros
-    assert TransitionKey.ACTION not in processed or processed[TransitionKey.ACTION] is None
+    assert processed[TransitionKey.ACTION].shape == (1, 5)
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available")