chore: replace hard-coded action values with constants throughout all the source code (#2055)

* chore: replace hard-coded 'action' values with constants throughout all the source code

* chore(tests): replace hard-coded action values with constants throughout all the test code

tests/processor/test_batch_conversion.py

@@ -2,7 +2,7 @@ import torch
 
 from lerobot.processor import DataProcessorPipeline, TransitionKey
 from lerobot.processor.converters import batch_to_transition, transition_to_batch
-from lerobot.utils.constants import OBS_IMAGE, OBS_PREFIX, OBS_STATE
+from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_PREFIX, OBS_STATE
 
 
 def _dummy_batch():
@@ -11,7 +11,7 @@ def _dummy_batch():
         f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128),
         f"{OBS_IMAGE}.right": torch.randn(1, 3, 128, 128),
         OBS_STATE: torch.tensor([[0.1, 0.2, 0.3, 0.4]]),
-        "action": torch.tensor([[0.5]]),
+        ACTION: torch.tensor([[0.5]]),
         "next.reward": 1.0,
         "next.done": False,
         "next.truncated": False,
@@ -37,7 +37,7 @@ def test_observation_grouping_roundtrip():
     assert torch.allclose(batch_out[OBS_STATE], batch_in[OBS_STATE])
 
     # Check other fields
-    assert torch.allclose(batch_out["action"], batch_in["action"])
+    assert torch.allclose(batch_out[ACTION], batch_in[ACTION])
     assert batch_out["next.reward"] == batch_in["next.reward"]
     assert batch_out["next.done"] == batch_in["next.done"]
     assert batch_out["next.truncated"] == batch_in["next.truncated"]
@@ -50,7 +50,7 @@ def test_batch_to_transition_observation_grouping():
         f"{OBS_IMAGE}.top": torch.randn(1, 3, 128, 128),
         f"{OBS_IMAGE}.left": torch.randn(1, 3, 128, 128),
         OBS_STATE: [1, 2, 3, 4],
-        "action": torch.tensor([0.1, 0.2, 0.3, 0.4]),
+        ACTION: torch.tensor([0.1, 0.2, 0.3, 0.4]),
         "next.reward": 1.5,
         "next.done": True,
         "next.truncated": False,
@@ -114,7 +114,7 @@ def test_transition_to_batch_observation_flattening():
     assert batch[OBS_STATE] == [1, 2, 3, 4]
 
     # Check other fields are mapped to next.* format
-    assert batch["action"] == "action_data"
+    assert batch[ACTION] == "action_data"
     assert batch["next.reward"] == 1.5
     assert batch["next.done"]
     assert not batch["next.truncated"]
@@ -124,7 +124,7 @@ def test_transition_to_batch_observation_flattening():
 def test_no_observation_keys():
     """Test behavior when there are no observation.* keys."""
     batch = {
-        "action": torch.tensor([1.0, 2.0]),
+        ACTION: torch.tensor([1.0, 2.0]),
         "next.reward": 2.0,
         "next.done": False,
         "next.truncated": True,
@@ -145,7 +145,7 @@ def test_no_observation_keys():
 
     # Round trip should work
     reconstructed_batch = transition_to_batch(transition)
-    assert torch.allclose(reconstructed_batch["action"], torch.tensor([1.0, 2.0]))
+    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"]
@@ -154,7 +154,7 @@ def test_no_observation_keys():
 
 def test_minimal_batch():
     """Test with minimal batch containing only observation.* and action."""
-    batch = {OBS_STATE: "minimal_state", "action": torch.tensor([0.5])}
+    batch = {OBS_STATE: "minimal_state", ACTION: torch.tensor([0.5])}
 
     transition = batch_to_transition(batch)
 
@@ -172,7 +172,7 @@ def test_minimal_batch():
     # Round trip
     reconstructed_batch = transition_to_batch(transition)
     assert reconstructed_batch[OBS_STATE] == "minimal_state"
-    assert torch.allclose(reconstructed_batch["action"], torch.tensor([0.5]))
+    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"]
@@ -196,7 +196,7 @@ def test_empty_batch():
 
     # Round trip
     reconstructed_batch = transition_to_batch(transition)
-    assert reconstructed_batch["action"] is None
+    assert reconstructed_batch[ACTION] is None
     assert reconstructed_batch["next.reward"] == 0.0
     assert not reconstructed_batch["next.done"]
     assert not reconstructed_batch["next.truncated"]
@@ -209,7 +209,7 @@ def test_complex_nested_observation():
         f"{OBS_IMAGE}.top": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567890},
         f"{OBS_IMAGE}.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891},
         OBS_STATE: torch.randn(7),
-        "action": torch.randn(8),
+        ACTION: torch.randn(8),
         "next.reward": 3.14,
         "next.done": False,
         "next.truncated": True,
@@ -237,7 +237,7 @@ def test_complex_nested_observation():
     )
 
     # Check action tensor
-    assert torch.allclose(batch["action"], reconstructed_batch["action"])
+    assert torch.allclose(batch[ACTION], reconstructed_batch[ACTION])
 
     # Check other fields
     assert batch["next.reward"] == reconstructed_batch["next.reward"]
@@ -266,7 +266,7 @@ def test_custom_converter():
 
     batch = {
         OBS_STATE: torch.randn(1, 4),
-        "action": torch.randn(1, 2),
+        ACTION: torch.randn(1, 2),
         "next.reward": 1.0,
         "next.done": False,
     }
@@ -276,4 +276,4 @@ def test_custom_converter():
     # Check the reward was doubled by our custom converter
     assert result["next.reward"] == 2.0
     assert torch.allclose(result[OBS_STATE], batch[OBS_STATE])
-    assert torch.allclose(result["action"], batch["action"])
+    assert torch.allclose(result[ACTION], batch[ACTION])