fix(processor): rename temporal padding metadata

src/lerobot/processor/rename_processor.py

@@ -21,6 +21,23 @@ from lerobot.configs import PipelineFeatureType, PolicyFeature
 
 from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
 
+_AUXILIARY_KEY_SUFFIXES = ("_is_pad", "_padding_mask")
+
+
+def rename_transition_key(key: str, rename_map: dict[str, str]) -> str:
+    """Rename a feature key and any sampling metadata derived from it."""
+    if key in rename_map:
+        return rename_map[key]
+    for suffix in _AUXILIARY_KEY_SUFFIXES:
+        if key.endswith(suffix) and key[: -len(suffix)] in rename_map:
+            return f"{rename_map[key[: -len(suffix)]]}{suffix}"
+    return key
+
+
+def rename_transition_keys(data: dict[str, Any], rename_map: dict[str, str]) -> dict[str, Any]:
+    """Rename all transition keys, including delta-sampling padding masks."""
+    return {rename_transition_key(key, rename_map): value for key, value in data.items()}
+
 
 @dataclass
 @ProcessorStepRegistry.register(name="rename_observations_processor")
@@ -41,14 +58,7 @@ class RenameObservationsProcessorStep(ObservationProcessorStep):
     rename_map: dict[str, str] = field(default_factory=dict)
 
     def observation(self, observation):
-        processed_obs = {}
+        return rename_transition_keys(observation, self.rename_map)
-        for key, value in observation.items():
-            if key in self.rename_map:
-                processed_obs[self.rename_map[key]] = value
-            else:
-                processed_obs[key] = value
-
-        return processed_obs
 
     def get_config(self) -> dict[str, Any]:
         return {"rename_map": self.rename_map}

src/lerobot/scripts/lerobot_train.py

@@ -57,6 +57,7 @@ from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
 from lerobot.jobs import submit_to_hf
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
+from lerobot.processor.rename_processor import rename_transition_keys
 from lerobot.rewards import make_reward_pre_post_processors
 from lerobot.utils.collate import lerobot_collate_fn
 from lerobot.utils.import_utils import register_third_party_plugins
@@ -610,7 +611,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
             if cam_key in batch and batch[cam_key].dtype == torch.uint8:
                 batch[cam_key] = batch[cam_key].to(dtype=torch.float32) / 255.0
         if cfg.rename_map:
-            batch = {cfg.rename_map.get(key, key): value for key, value in batch.items()}
+            batch = rename_transition_keys(batch, cfg.rename_map)
         batch = preprocessor(batch)
         train_tracker.dataloading_s = time.perf_counter() - start_time
 

tests/processor/test_rename_processor.py

@@ -27,7 +27,7 @@ from lerobot.processor import (
     TransitionKey,
 )
 from lerobot.processor.converters import create_transition, identity_transition
-from lerobot.processor.rename_processor import rename_stats
+from lerobot.processor.rename_processor import rename_stats, rename_transition_keys
 from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_IMAGES, OBS_STATE
 from tests.conftest import assert_contract_is_typed
 
@@ -64,6 +64,22 @@ def test_basic_renaming():
     assert processed_obs["unchanged_key"] == "keep_me"
 
 
+def test_renaming_preserves_feature_suffixes_for_sampling_metadata():
+    data = {
+        "image": torch.zeros(1),
+        "image_is_pad": torch.ones(1, dtype=torch.bool),
+        "image_padding_mask": torch.ones(1, dtype=torch.bool),
+    }
+
+    result = rename_transition_keys(data, {"image": "observation.images.camera1"})
+
+    assert set(result) == {
+        "observation.images.camera1",
+        "observation.images.camera1_is_pad",
+        "observation.images.camera1_padding_mask",
+    }
+
+
 def test_empty_rename_map():
     """Test processor with empty rename map (should pass through unchanged)."""
     processor = RenameObservationsProcessorStep(rename_map={})