temp_training

src/lerobot/datasets/aggregate.py

@@ -72,10 +72,11 @@ def validate_all_metadata(all_metadata: list[LeRobotDatasetMetadata]):
             raise ValueError(
                 f"Same robot_type is expected, but got robot_type={meta.robot_type} instead of {robot_type}."
             )
-        if features != meta.features:
-            raise ValueError(
-                f"Same features is expected, but got features={meta.features} instead of {features}."
-            )
+        # TODO: Temporarily disabled for merging datasets with different features (e.g. shirt_id)
+        # if features != meta.features:
+        #     raise ValueError(
+        #         f"Same features is expected, but got features={meta.features} instead of {features}."
+        #     )
 
     return fps, robot_type, features
 

src/lerobot/datasets/lerobot_dataset.py

@@ -560,7 +560,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         episodes: list[int] | None = None,
         image_transforms: Callable | None = None,
         delta_timestamps: dict[str, list[float]] | None = None,
-        tolerance_s: float = 1e-4,
+        tolerance_s: float = 1e-2,
         revision: str | None = None,
         force_cache_sync: bool = False,
         download_videos: bool = True,
@@ -1563,7 +1563,7 @@ class LeRobotDataset(torch.utils.data.Dataset):
         root: str | Path | None = None,
         robot_type: str | None = None,
         use_videos: bool = True,
-        tolerance_s: float = 1e-4,
+        tolerance_s: float = 1e-2,
         image_writer_processes: int = 0,
         image_writer_threads: int = 0,
         video_backend: str | None = None,

src/lerobot/policies/pi05/configuration_pi05.py

@@ -61,8 +61,6 @@ class PI05Config(PreTrainedConfig):
     # Add empty images. Used to add empty cameras when no image features are present.
     empty_cameras: int = 0
 
-    tokenizer_max_length: int = 200  # see openpi `__post_init__`
-
     normalization_mapping: dict[str, NormalizationMode] = field(
         default_factory=lambda: {
             "VISUAL": NormalizationMode.IDENTITY,

src/lerobot/scripts/lerobot_train.py

@@ -337,13 +337,28 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         logging.info(f"{num_total_params=} ({format_big_number(num_total_params)})")
 
     # create dataloader for offline training
-    if hasattr(cfg.policy, "drop_n_last_frames"):
+    # Filter out episodes - hardcoded list of bad episodes to discard
+    episodes_to_discard = {
+        133, 134, 502, 565, 568, 657, 910, 944, 1039, 1209, 1346, 1360, 1379,
+        1605, 1690, 1790, 2105, 2106, 2122, 2118, 2156, 2575, 2764, 2876, 2925,
+        3100, 3381, 3405, 3406, 68, 1214, 1456,
+    }
+    all_episodes = set(range(dataset.meta.total_episodes))
+    episodes_to_use = dataset.episodes  # May be None (all episodes) or a subset
+    # If dataset.episodes is already filtered, start from that subset
+    if episodes_to_use is not None:
+        episodes_to_use = [ep for ep in episodes_to_use if ep not in episodes_to_discard]
+    else:
+        episodes_to_use = sorted(all_episodes - episodes_to_discard)
+
+    if hasattr(cfg.policy, "drop_n_last_frames") or episodes_to_use is not None:
         shuffle = False
+        drop_n_last = getattr(cfg.policy, "drop_n_last_frames", 0)
         sampler = EpisodeAwareSampler(
             dataset.meta.episodes["dataset_from_index"],
             dataset.meta.episodes["dataset_to_index"],
-            episode_indices_to_use=dataset.episodes,
-            drop_n_last_frames=cfg.policy.drop_n_last_frames,
+            episode_indices_to_use=episodes_to_use,
+            drop_n_last_frames=drop_n_last,
             shuffle=True,
         )
     else: