chore: replace hard-coded next values with constants throughout all the source code (#2056)

tests/datasets/test_datasets.py

@@ -46,7 +46,7 @@ from lerobot.datasets.utils import (
 from lerobot.envs.factory import make_env_config
 from lerobot.policies.factory import make_policy_config
 from lerobot.robots import make_robot_from_config
-from lerobot.utils.constants import ACTION, OBS_IMAGES, OBS_STATE, OBS_STR
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD
 from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
 from tests.mocks.mock_robot import MockRobotConfig
 from tests.utils import require_x86_64_kernel
@@ -399,8 +399,8 @@ def test_factory(env_name, repo_id, policy_name):
         ("timestamp", 0, True),
         # TODO(rcadene): should we rename it agent_pos?
         (OBS_STATE, 1, True),
-        ("next.reward", 0, False),
-        ("next.done", 0, False),
+        (REWARD, 0, False),
+        (DONE, 0, False),
     ]
 
     # test number of dimensions

tests/policies/hilserl/test_modeling_classifier.py

@@ -19,7 +19,7 @@ import torch
 from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
 from lerobot.policies.sac.reward_model.modeling_classifier import ClassifierOutput
-from lerobot.utils.constants import OBS_IMAGE
+from lerobot.utils.constants import OBS_IMAGE, REWARD
 from tests.utils import require_package
 
 
@@ -45,7 +45,7 @@ def test_binary_classifier_with_default_params():
         OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
     }
     config.output_features = {
-        "next.reward": PolicyFeature(type=FeatureType.REWARD, shape=(1,)),
+        REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(1,)),
     }
     config.normalization_mapping = {
         "VISUAL": NormalizationMode.IDENTITY,
@@ -58,7 +58,7 @@ def test_binary_classifier_with_default_params():
 
     input = {
         OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)),
-        "next.reward": torch.randint(low=0, high=2, size=(batch_size,)).float(),
+        REWARD: torch.randint(low=0, high=2, size=(batch_size,)).float(),
     }
 
     images, labels = classifier.extract_images_and_labels(input)
@@ -87,7 +87,7 @@ def test_multiclass_classifier():
         OBS_IMAGE: PolicyFeature(type=FeatureType.VISUAL, shape=(3, 224, 224)),
     }
     config.output_features = {
-        "next.reward": PolicyFeature(type=FeatureType.REWARD, shape=(num_classes,)),
+        REWARD: PolicyFeature(type=FeatureType.REWARD, shape=(num_classes,)),
     }
     config.num_cameras = 1
     config.num_classes = num_classes
@@ -97,7 +97,7 @@ def test_multiclass_classifier():
 
     input = {
         OBS_IMAGE: torch.rand((batch_size, 3, 128, 128)),
-        "next.reward": torch.rand((batch_size, num_classes)),
+        REWARD: torch.rand((batch_size, num_classes)),
     }
 
     images, labels = classifier.extract_images_and_labels(input)

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 ACTION, OBS_IMAGE, OBS_PREFIX, OBS_STATE
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_PREFIX, OBS_STATE, REWARD, TRUNCATED
 
 
 def _dummy_batch():
@@ -12,9 +12,9 @@ def _dummy_batch():
         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]]),
-        "next.reward": 1.0,
-        "next.done": False,
-        "next.truncated": False,
+        REWARD: 1.0,
+        DONE: False,
+        TRUNCATED: False,
         "info": {"key": "value"},
     }
 
@@ -38,9 +38,9 @@ def test_observation_grouping_roundtrip():
 
     # Check other fields
     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"]
+    assert batch_out[REWARD] == batch_in[REWARD]
+    assert batch_out[DONE] == batch_in[DONE]
+    assert batch_out[TRUNCATED] == batch_in[TRUNCATED]
     assert batch_out["info"] == batch_in["info"]
 
 
@@ -51,9 +51,9 @@ def test_batch_to_transition_observation_grouping():
         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]),
-        "next.reward": 1.5,
-        "next.done": True,
-        "next.truncated": False,
+        REWARD: 1.5,
+        DONE: True,
+        TRUNCATED: False,
         "info": {"episode": 42},
     }
 
@@ -115,9 +115,9 @@ def test_transition_to_batch_observation_flattening():
 
     # Check other fields are mapped to next.* format
     assert batch[ACTION] == "action_data"
-    assert batch["next.reward"] == 1.5
-    assert batch["next.done"]
-    assert not batch["next.truncated"]
+    assert batch[REWARD] == 1.5
+    assert batch[DONE]
+    assert not batch[TRUNCATED]
     assert batch["info"] == {"episode": 42}
 
 
@@ -125,9 +125,9 @@ def test_no_observation_keys():
     """Test behavior when there are no observation.* keys."""
     batch = {
         ACTION: torch.tensor([1.0, 2.0]),
-        "next.reward": 2.0,
-        "next.done": False,
-        "next.truncated": True,
+        REWARD: 2.0,
+        DONE: False,
+        TRUNCATED: True,
         "info": {"test": "no_obs"},
     }
 
@@ -146,9 +146,9 @@ 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 reconstructed_batch["next.reward"] == 2.0
-    assert not reconstructed_batch["next.done"]
-    assert reconstructed_batch["next.truncated"]
+    assert reconstructed_batch[REWARD] == 2.0
+    assert not reconstructed_batch[DONE]
+    assert reconstructed_batch[TRUNCATED]
     assert reconstructed_batch["info"] == {"test": "no_obs"}
 
 
@@ -173,9 +173,9 @@ def test_minimal_batch():
     reconstructed_batch = transition_to_batch(transition)
     assert reconstructed_batch[OBS_STATE] == "minimal_state"
     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"]
+    assert reconstructed_batch[REWARD] == 0.0
+    assert not reconstructed_batch[DONE]
+    assert not reconstructed_batch[TRUNCATED]
     assert reconstructed_batch["info"] == {}
 
 
@@ -197,9 +197,9 @@ def test_empty_batch():
     # Round trip
     reconstructed_batch = transition_to_batch(transition)
     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"]
+    assert reconstructed_batch[REWARD] == 0.0
+    assert not reconstructed_batch[DONE]
+    assert not reconstructed_batch[TRUNCATED]
     assert reconstructed_batch["info"] == {}
 
 
@@ -210,9 +210,9 @@ def test_complex_nested_observation():
         f"{OBS_IMAGE}.left": {"image": torch.randn(1, 3, 128, 128), "timestamp": 1234567891},
         OBS_STATE: torch.randn(7),
         ACTION: torch.randn(8),
-        "next.reward": 3.14,
-        "next.done": False,
-        "next.truncated": True,
+        REWARD: 3.14,
+        DONE: False,
+        TRUNCATED: True,
         "info": {"episode_length": 200, "success": True},
     }
 
@@ -240,9 +240,9 @@ def test_complex_nested_observation():
     assert torch.allclose(batch[ACTION], reconstructed_batch[ACTION])
 
     # Check other fields
-    assert batch["next.reward"] == reconstructed_batch["next.reward"]
-    assert batch["next.done"] == reconstructed_batch["next.done"]
-    assert batch["next.truncated"] == reconstructed_batch["next.truncated"]
+    assert batch[REWARD] == reconstructed_batch[REWARD]
+    assert batch[DONE] == reconstructed_batch[DONE]
+    assert batch[TRUNCATED] == reconstructed_batch[TRUNCATED]
     assert batch["info"] == reconstructed_batch["info"]
 
 
@@ -267,13 +267,13 @@ def test_custom_converter():
     batch = {
         OBS_STATE: torch.randn(1, 4),
         ACTION: torch.randn(1, 2),
-        "next.reward": 1.0,
-        "next.done": False,
+        REWARD: 1.0,
+        DONE: False,
     }
 
     result = processor(batch)
 
     # Check the reward was doubled by our custom converter
-    assert result["next.reward"] == 2.0
+    assert result[REWARD] == 2.0
     assert torch.allclose(result[OBS_STATE], batch[OBS_STATE])
     assert torch.allclose(result[ACTION], batch[ACTION])

tests/processor/test_converters.py

@@ -9,7 +9,7 @@ from lerobot.processor.converters import (
     to_tensor,
     transition_to_batch,
 )
-from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
+from lerobot.utils.constants import ACTION, DONE, OBS_STATE, OBS_STR, REWARD
 
 
 # Tests for the unified to_tensor function
@@ -201,8 +201,8 @@ def test_batch_to_transition_with_index_fields():
     batch = {
         OBS_STATE: torch.randn(1, 7),
         ACTION: torch.randn(1, 4),
-        "next.reward": 1.5,
-        "next.done": False,
+        REWARD: 1.5,
+        DONE: False,
         "task": ["pick_cube"],
         "index": torch.tensor([42], dtype=torch.int64),
         "task_index": torch.tensor([3], dtype=torch.int64),

tests/processor/test_pipeline.py

@@ -35,7 +35,7 @@ from lerobot.processor import (
     TransitionKey,
 )
 from lerobot.processor.converters import create_transition, identity_transition
-from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_IMAGES, OBS_STATE
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STATE, REWARD, TRUNCATED
 from tests.conftest import assert_contract_is_typed
 
 
@@ -258,9 +258,9 @@ def test_step_through_with_dict():
     batch = {
         OBS_IMAGE: None,
         ACTION: None,
-        "next.reward": 0.0,
-        "next.done": False,
-        "next.truncated": False,
+        REWARD: 0.0,
+        DONE: False,
+        TRUNCATED: False,
         "info": {},
     }
 
@@ -1843,9 +1843,9 @@ def test_save_load_with_custom_converter_functions():
         batch = {
             OBS_IMAGE: torch.randn(1, 3, 32, 32),
             ACTION: torch.randn(1, 7),
-            "next.reward": torch.tensor([1.0]),
-            "next.done": torch.tensor([False]),
-            "next.truncated": torch.tensor([False]),
+            REWARD: torch.tensor([1.0]),
+            DONE: torch.tensor([False]),
+            TRUNCATED: torch.tensor([False]),
             "info": {},
         }
 

tests/utils/test_replay_buffer.py

@@ -22,7 +22,7 @@ import torch
 
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.rl.buffer import BatchTransition, ReplayBuffer, random_crop_vectorized
-from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE, OBS_STR
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_STATE, OBS_STR, REWARD
 from tests.fixtures.constants import DUMMY_REPO_ID
 
 
@@ -380,9 +380,9 @@ def test_to_lerobot_dataset(tmp_path):
         for feature, value in ds[i].items():
             if feature == ACTION:
                 assert torch.equal(value, buffer.actions[i])
-            elif feature == "next.reward":
+            elif feature == REWARD:
                 assert torch.equal(value, buffer.rewards[i])
-            elif feature == "next.done":
+            elif feature == DONE:
                 assert torch.equal(value, buffer.dones[i])
             elif feature == OBS_IMAGE:
                 # Tensor -> numpy is not precise, so we have some diff there