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>

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"]