fix: tests

src/lerobot/datasets/aggregate.py

@@ -15,8 +15,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import contextlib
 import logging
 import shutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 
 import pandas as pd
@@ -107,6 +109,7 @@ def update_meta_data(
     dst_meta,
     meta_idx,
     data_idx,
+    data_file_map,
     videos_idx,
 ):
     """Updates metadata DataFrame with new chunk, file, and timestamp indices.
@@ -127,8 +130,25 @@ def update_meta_data(
 
     df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"]
     df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"]
-    df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
-    df["data/file_index"] = df["data/file_index"] + data_idx["file"]
+    # Remap data chunk/file indices per-source-file using the actual destination
+    # file chosen during data aggregation. A flat offset is incorrect when
+    # multiple source files are concatenated into a single destination file.
+    if data_file_map:
+        new_data_chunk = []
+        new_data_file = []
+        for idx in df.index:
+            src_chunk = int(df.at[idx, "data/chunk_index"])  # original source file location
+            src_file = int(df.at[idx, "data/file_index"])  # original source file location
+            dst_chunk, dst_file = data_file_map.get(
+                (src_chunk, src_file), (src_chunk + data_idx["chunk"], src_file + data_idx["file"])
+            )
+            new_data_chunk.append(dst_chunk)
+            new_data_file.append(dst_file)
+        df["data/chunk_index"] = new_data_chunk
+        df["data/file_index"] = new_data_file
+    else:
+        df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
+        df["data/file_index"] = df["data/file_index"] + data_idx["file"]
     for key, video_idx in videos_idx.items():
         # Store original video file indices before updating
         orig_chunk_col = f"videos/{key}/chunk_index"
@@ -166,7 +186,7 @@ def update_meta_data(
     return df
 
 
-def aggregate_datasets(
+def _aggregate_datasets(
     repo_ids: list[str],
     aggr_repo_id: str,
     roots: list[Path] | None = None,
@@ -175,39 +195,24 @@ def aggregate_datasets(
     video_files_size_in_mb: float | None = None,
     chunk_size: int | None = None,
 ):
-    """Aggregates multiple LeRobot datasets into a single unified dataset.
+    """Serial aggregation kernel: combines datasets into a destination dataset.
 
-    This is the main function that orchestrates the aggregation process by:
-    1. Loading and validating all source dataset metadata
-    2. Creating a new destination dataset with unified tasks
-    3. Aggregating videos, data, and metadata from all source datasets
-    4. Finalizing the aggregated dataset with proper statistics
-
-    Args:
-        repo_ids: List of repository IDs for the datasets to aggregate.
-        aggr_repo_id: Repository ID for the aggregated output dataset.
-        roots: Optional list of root paths for the source datasets.
-        aggr_root: Optional root path for the aggregated dataset.
-        data_files_size_in_mb: Maximum size for data files in MB (defaults to DEFAULT_DATA_FILE_SIZE_IN_MB)
-        video_files_size_in_mb: Maximum size for video files in MB (defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB)
-        chunk_size: Maximum number of files per chunk (defaults to DEFAULT_CHUNK_SIZE)
+    This function performs a single-process aggregation. It assumes it is the
+    sole writer for its destination `aggr_root`.
     """
-    logging.info("Start aggregate_datasets")
-
-    if data_files_size_in_mb is None:
-        data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
-    if video_files_size_in_mb is None:
-        video_files_size_in_mb = DEFAULT_VIDEO_FILE_SIZE_IN_MB
-    if chunk_size is None:
-        chunk_size = DEFAULT_CHUNK_SIZE
-
-    all_metadata = (
-        [LeRobotDatasetMetadata(repo_id) for repo_id in repo_ids]
-        if roots is None
-        else [
-            LeRobotDatasetMetadata(repo_id, root=root) for repo_id, root in zip(repo_ids, roots, strict=False)
+    # Build metadata objects, supporting a per-dataset "root" that may be None.
+    # When root is provided we load from the local filesystem, otherwise from Hub cache.
+    if roots is None:
+        all_metadata = [LeRobotDatasetMetadata(repo_id) for repo_id in repo_ids]
+    else:
+        all_metadata = [
+            (
+                LeRobotDatasetMetadata(repo_id, root=root)
+                if root is not None
+                else LeRobotDatasetMetadata(repo_id)
+            )
+            for repo_id, root in zip(repo_ids, roots, strict=False)
         ]
-    )
     fps, robot_type, features = validate_all_metadata(all_metadata)
     video_keys = [key for key in features if features[key]["dtype"] == "video"]
 
@@ -237,9 +242,11 @@ def aggregate_datasets(
 
     for src_meta in tqdm.tqdm(all_metadata, desc="Copy data and videos"):
         videos_idx = aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size)
-        data_idx = aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size)
+        data_idx, data_file_map = aggregate_data(
+            src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_size
+        )
 
-        meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx)
+        meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, data_file_map, videos_idx)
 
         dst_meta.info["total_episodes"] += src_meta.total_episodes
         dst_meta.info["total_frames"] += src_meta.total_frames
@@ -248,6 +255,168 @@ def aggregate_datasets(
     logging.info("Aggregation complete.")
 
 
+def aggregate_datasets(
+    repo_ids: list[str],
+    aggr_repo_id: str,
+    roots: list[Path] | None = None,
+    aggr_root: Path | None = None,
+    data_files_size_in_mb: float | None = None,
+    video_files_size_in_mb: float | None = None,
+    chunk_size: int | None = None,
+    num_workers: int | None = None,
+    tmp_root: Path | None = None,
+):
+    """Aggregates multiple LeRobot datasets into a single unified dataset.
+
+    This is the main function that orchestrates the aggregation process by:
+    1. Loading and validating all source dataset metadata
+    2. Creating a new destination dataset with unified tasks
+    3. Aggregating videos, data, and metadata from all source datasets
+    4. Finalizing the aggregated dataset with proper statistics
+
+    Args:
+        repo_ids: List of repository IDs for the datasets to aggregate.
+        aggr_repo_id: Repository ID for the aggregated output dataset.
+        roots: Optional list of root paths for the source datasets.
+        aggr_root: Optional root path for the aggregated dataset.
+        data_files_size_in_mb: Maximum size for data files in MB (defaults to DEFAULT_DATA_FILE_SIZE_IN_MB)
+        video_files_size_in_mb: Maximum size for video files in MB (defaults to DEFAULT_VIDEO_FILE_SIZE_IN_MB)
+        chunk_size: Maximum number of files per chunk (defaults to DEFAULT_CHUNK_SIZE)
+        num_workers: When > 1, performs a tree-based parallel reduction using a thread pool
+        tmp_root: Optional base directory to store intermediate reduction outputs
+    """
+    logging.info("Start aggregate_datasets")
+
+    if data_files_size_in_mb is None:
+        data_files_size_in_mb = DEFAULT_DATA_FILE_SIZE_IN_MB
+    if video_files_size_in_mb is None:
+        video_files_size_in_mb = DEFAULT_VIDEO_FILE_SIZE_IN_MB
+    if chunk_size is None:
+        chunk_size = DEFAULT_CHUNK_SIZE
+
+    if num_workers is None or num_workers <= 1:
+        # Run aggregation sequentially
+        _aggregate_datasets(
+            repo_ids=repo_ids,
+            aggr_repo_id=aggr_repo_id,
+            aggr_root=aggr_root,
+            roots=roots,
+            data_files_size_in_mb=data_files_size_in_mb,
+            video_files_size_in_mb=video_files_size_in_mb,
+            chunk_size=chunk_size,
+        )
+
+    # Uses a parallel fan-out/fan-in strategy when num_workers is provided
+    elif num_workers > 1:
+        # Validate across all metadata early to fail fast
+        all_metadata_for_validation = (
+            [LeRobotDatasetMetadata(repo_id) for repo_id in repo_ids]
+            if roots is None
+            else [
+                LeRobotDatasetMetadata(repo_id, root=root)
+                for repo_id, root in zip(repo_ids, roots, strict=False)
+            ]
+        )
+        validate_all_metadata(all_metadata_for_validation)
+
+        # Clamp workers to a sensible upper bound (pairs per round)
+        num_workers = min(num_workers, max(1, len(repo_ids) // 2))
+
+        # Choose a base temporary root for intermediate merge results
+        if tmp_root is not None:
+            base_tmp_root = tmp_root
+        elif aggr_root is not None:
+            base_tmp_root = aggr_root.parent / f".{aggr_repo_id}__tmp"
+        else:
+            base_tmp_root = Path.cwd() / f".{aggr_repo_id}__tmp"
+        base_tmp_root.mkdir(parents=True, exist_ok=True)
+
+        current_repo_ids: list[str] = list(repo_ids)
+        # Always maintain a roots list aligned with repo_ids. Use None for Hub-backed inputs.
+        current_roots: list[Path | None] = list(roots) if roots is not None else [None] * len(repo_ids)
+
+        try:
+            level = 0
+            while len(current_repo_ids) > 1:
+                next_repo_ids: list[str] = []
+                next_roots: list[Path | None] = []
+                futures = []
+
+                with ThreadPoolExecutor(max_workers=num_workers) as executor:
+                    group_index = 0
+                    i = 0
+                    while i < len(current_repo_ids):
+                        group_repo_ids = current_repo_ids[i : i + 2]
+                        group_roots = current_roots[i : i + 2]
+
+                        if len(group_repo_ids) == 1:
+                            # Carry over singleton to next level
+                            next_repo_ids.append(group_repo_ids[0])
+                            next_roots.append(group_roots[0])
+                            i += 1
+                            continue
+
+                        out_repo_id = f"{aggr_repo_id}__reduce_l{level}_g{group_index}"
+                        out_root = base_tmp_root / f"reduce_l{level}_g{group_index}"
+
+                        futures.append(
+                            executor.submit(
+                                _aggregate_datasets,
+                                group_repo_ids,
+                                out_repo_id,
+                                group_roots,
+                                out_root,
+                                data_files_size_in_mb,
+                                video_files_size_in_mb,
+                                chunk_size,
+                            )
+                        )
+
+                        next_repo_ids.append(out_repo_id)
+                        next_roots.append(out_root)
+
+                        i += 2
+                        group_index += 1
+
+                    for f in as_completed(futures):
+                        # Bubble up any exception raised inside tasks
+                        f.result()
+
+                # Cleanup previous level temporary outputs that won't be used again
+                base_resolved = base_tmp_root.resolve()
+                keep_set = {nr.resolve() for nr in next_roots if nr is not None}
+                for prev_root in current_roots:
+                    if prev_root is None:
+                        continue
+                    # Suppress per-iteration to keep cleaning other roots even if one fails
+                    with contextlib.suppress(Exception):
+                        pr = prev_root.resolve()
+                        if pr not in keep_set and base_resolved in pr.parents:
+                            shutil.rmtree(prev_root, ignore_errors=True)
+
+                current_repo_ids = next_repo_ids
+                current_roots = next_roots  # aligned list of Path|None after first level
+                level += 1
+
+            # Final copy/aggregation into the desired output
+            _aggregate_datasets(
+                repo_ids=current_repo_ids,
+                aggr_repo_id=aggr_repo_id,
+                roots=current_roots,
+                aggr_root=aggr_root,
+                data_files_size_in_mb=data_files_size_in_mb,
+                video_files_size_in_mb=video_files_size_in_mb,
+                chunk_size=chunk_size,
+            )
+        finally:
+            # Remove all temporary reduction artifacts
+            with contextlib.suppress(Exception):
+                shutil.rmtree(base_tmp_root, ignore_errors=True)
+
+        logging.info("Aggregation complete.")
+        return
+
+
 def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chunk_size):
     """Aggregates video chunks from a source dataset into the destination dataset.
 
@@ -366,6 +535,9 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
 
     unique_chunk_file_ids = sorted(unique_chunk_file_ids)
 
+    # Map source (chunk,file) -> destination (chunk,file) actually used during write
+    src_to_dst_file: dict[tuple[int, int], tuple[int, int]] = {}
+
     for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
             chunk_index=src_chunk_idx, file_index=src_file_idx
@@ -373,7 +545,7 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
         df = pd.read_parquet(src_path)
         df = update_data_df(df, src_meta, dst_meta)
 
-        data_idx = append_or_create_parquet_file(
+        data_idx, used_chunk, used_file = append_or_create_parquet_file(
             df,
             src_path,
             data_idx,
@@ -383,11 +555,12 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
             contains_images=len(dst_meta.image_keys) > 0,
             aggr_root=dst_meta.root,
         )
+        src_to_dst_file[(src_chunk_idx, src_file_idx)] = (used_chunk, used_file)
 
-    return data_idx
+    return data_idx, src_to_dst_file
 
 
-def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
+def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, data_file_map, videos_idx):
     """Aggregates metadata from a source dataset into the destination dataset.
 
     Reads source metadata files, updates all indices and timestamps,
@@ -421,10 +594,11 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             dst_meta,
             meta_idx,
             data_idx,
+            data_file_map,
             videos_idx,
         )
 
-        meta_idx = append_or_create_parquet_file(
+        meta_idx, _m_used_chunk, _m_used_file = append_or_create_parquet_file(
             df,
             src_path,
             meta_idx,
@@ -478,7 +652,7 @@ def append_or_create_parquet_file(
             to_parquet_with_hf_images(df, dst_path)
         else:
             df.to_parquet(dst_path)
-        return idx
+        return idx, idx["chunk"], idx["file"]
 
     src_size = get_parquet_file_size_in_mb(src_path)
     dst_size = get_parquet_file_size_in_mb(dst_path)
@@ -489,17 +663,19 @@ def append_or_create_parquet_file(
         new_path.parent.mkdir(parents=True, exist_ok=True)
         final_df = df
         target_path = new_path
+        used_chunk, used_file = idx["chunk"], idx["file"]
     else:
         existing_df = pd.read_parquet(dst_path)
         final_df = pd.concat([existing_df, df], ignore_index=True)
         target_path = dst_path
+        used_chunk, used_file = idx["chunk"], idx["file"]
 
     if contains_images:
         to_parquet_with_hf_images(final_df, target_path)
     else:
         final_df.to_parquet(target_path)
 
-    return idx
+    return idx, used_chunk, used_file
 
 
 def finalize_aggregation(aggr_meta, all_metadata):

src/lerobot/datasets/dataset_tools.py

@@ -234,6 +234,7 @@ def merge_datasets(
     datasets: list[LeRobotDataset],
     output_repo_id: str,
     output_dir: str | Path | None = None,
+    num_workers: int | None = None,
 ) -> LeRobotDataset:
     """Merge multiple LeRobotDatasets into a single dataset.
 
@@ -257,6 +258,7 @@ def merge_datasets(
         aggr_repo_id=output_repo_id,
         roots=roots,
         aggr_root=output_dir,
+        num_workers=num_workers,
     )
 
     merged_dataset = LeRobotDataset(
@@ -329,7 +331,7 @@ def modify_features(
 
     if repo_id is None:
         repo_id = f"{dataset.repo_id}_modified"
-    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
+    output_dir = Path(output_dir, exists_ok=True) if output_dir is not None else HF_LEROBOT_HOME / repo_id
 
     new_features = dataset.meta.features.copy()
 

src/lerobot/datasets/lerobot_dataset.py

@@ -940,11 +940,26 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        return {
-            key: torch.stack(self.hf_dataset[q_idx][key])
-            for key, q_idx in query_indices.items()
-            if key not in self.meta.video_keys
-        }
+        """
+        Query dataset for indices across keys, skipping video keys.
+
+        Tries column-first [key][indices] for speed, falls back to row-first.
+
+        Args:
+            query_indices: Dict mapping keys to index lists to retrieve
+
+        Returns:
+            Dict with stacked tensors of queried data (video keys excluded)
+        """
+        result: dict = {}
+        for key, q_idx in query_indices.items():
+            if key in self.meta.video_keys:
+                continue
+            try:
+                result[key] = torch.stack(self.hf_dataset[key][q_idx])
+            except (KeyError, TypeError, IndexError):
+                result[key] = torch.stack(self.hf_dataset[q_idx][key])
+        return result
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -45,7 +45,7 @@ class DiffusionConfig(PreTrainedConfig):
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
-        chunk_size: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
+        horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
         n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
             See `DiffusionPolicy.select_action` for more details.
         input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
@@ -105,7 +105,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     # Inputs / output structure.
     n_obs_steps: int = 2
-    chunk_size: int = 16
+    horizon: int = 16
     n_action_steps: int = 8
 
     normalization_mapping: dict[str, NormalizationMode] = field(
@@ -118,7 +118,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     # The original implementation doesn't sample frames for the last 7 steps,
     # which avoids excessive padding and leads to improved training results.
-    drop_n_last_frames: int = 7  # chunk_size - n_action_steps - n_obs_steps + 1
+    drop_n_last_frames: int = 7  # horizon - n_action_steps - n_obs_steps + 1
 
     # Architecture / modeling.
     # Vision backbone.
@@ -180,13 +180,13 @@ class DiffusionConfig(PreTrainedConfig):
                 f"Got {self.noise_scheduler_type}."
             )
 
-        # Check that the chunk size and U-Net downsampling is compatible.
+        # Check that the horizon size and U-Net downsampling is compatible.
         # U-Net downsamples by 2 with each stage.
         downsampling_factor = 2 ** len(self.down_dims)
-        if self.chunk_size % downsampling_factor != 0:
+        if self.horizon % downsampling_factor != 0:
             raise ValueError(
-                "The chunk_size should be an integer multiple of the downsampling factor (which is determined "
-                f"by `len(down_dims)`). Got {self.chunk_size=} and {self.down_dims=}"
+                "The horizon should be an integer multiple of the downsampling factor (which is determined "
+                f"by `len(down_dims)`). Got {self.horizon=} and {self.down_dims=}"
             )
 
     def get_optimizer_preset(self) -> AdamConfig:
@@ -231,7 +231,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     @property
     def action_delta_indices(self) -> list:
-        return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.chunk_size))
+        return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.horizon))
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -99,25 +99,25 @@ class DiffusionPolicy(PreTrainedPolicy):
         return actions
 
     @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None, **kwargs) -> Tensor:
+    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
         """Select a single action given environment observations.
 
         This method handles caching a history of observations and an action trajectory generated by the
         underlying diffusion model. Here's how it works:
           - `n_obs_steps` steps worth of observations are cached (for the first steps, the observation is
             copied `n_obs_steps` times to fill the cache).
-          - The diffusion model generates `chunk_size` steps worth of actions.
+          - The diffusion model generates `horizon` steps worth of actions.
           - `n_action_steps` worth of actions are actually kept for execution, starting from the current step.
         Schematically this looks like:
             ----------------------------------------------------------------------------------------------
-            (legend: o = n_obs_steps, c = chunk_size, a = n_action_steps)
+            (legend: o = n_obs_steps, h = horizon, a = n_action_steps)
             |timestep            | n-o+1 | n-o+2 | ..... | n     | ..... | n+a-1 | n+a   | ..... | n-o+h |
             |observation is used | YES   | YES   | YES   | YES   | NO    | NO    | NO    | NO    | NO    |
             |action is generated | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   |
             |action is used      | NO    | NO    | NO    | YES   | YES   | YES   | NO    | NO    | NO    |
             ----------------------------------------------------------------------------------------------
-        Note that this means we require: `n_action_steps <= chunk_size - n_obs_steps + 1`. Also, note that
-        this period is
+        Note that this means we require: `n_action_steps <= horizon - n_obs_steps + 1`. Also, note that
+        "horizon" may not the best name to describe what the variable actually means, because this period is
         actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past.
         """
         # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out
@@ -213,7 +213,7 @@ class DiffusionModel(nn.Module):
             noise
             if noise is not None
             else torch.randn(
-                size=(batch_size, self.config.chunk_size, self.config.action_feature.shape[0]),
+                size=(batch_size, self.config.horizon, self.config.action_feature.shape[0]),
                 dtype=dtype,
                 device=device,
                 generator=generator,
@@ -309,16 +309,16 @@ class DiffusionModel(nn.Module):
                 AND/OR
             "observation.environment_state": (B, n_obs_steps, environment_dim)
 
-            "action": (B, chunk_size, action_dim)
-            "action_is_pad": (B, chunk_size)
+            "action": (B, horizon, action_dim)
+            "action_is_pad": (B, horizon)
         }
         """
         # Input validation.
         assert set(batch).issuperset({OBS_STATE, ACTION, "action_is_pad"})
         assert OBS_IMAGES in batch or OBS_ENV_STATE in batch
         n_obs_steps = batch[OBS_STATE].shape[1]
-        chunk_size = batch[ACTION].shape[1]
-        assert chunk_size == self.config.chunk_size
+        horizon = batch[ACTION].shape[1]
+        assert horizon == self.config.horizon
         assert n_obs_steps == self.config.n_obs_steps
 
         # Encode image features and concatenate them all together along with the state vector.

src/lerobot/policies/dsrl/configuration_dsrl.py

@@ -1,242 +0,0 @@
-# !/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass, field
-
-from lerobot.configs.policies import PreTrainedConfig
-from lerobot.configs.types import NormalizationMode
-from lerobot.optim.optimizers import MultiAdamConfig
-from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
-
-
-def is_image_feature(key: str) -> bool:
-    """Check if a feature key represents an image feature.
-
-    Args:
-        key: The feature key to check
-
-    Returns:
-        True if the key represents an image feature, False otherwise
-    """
-    return key.startswith(OBS_IMAGE)
-
-
-@dataclass
-class ConcurrencyConfig:
-    """Configuration for the concurrency of the actor and learner.
-    Possible values are:
-    - "threads": Use threads for the actor and learner.
-    - "processes": Use processes for the actor and learner.
-    """
-
-    actor: str = "threads"
-    learner: str = "threads"
-
-
-@dataclass
-class ActorLearnerConfig:
-    learner_host: str = "127.0.0.1"
-    learner_port: int = 50051
-    policy_parameters_push_frequency: int = 4
-    queue_get_timeout: float = 2
-
-
-@dataclass
-class CriticNetworkConfig:
-    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
-    activate_final: bool = True
-    final_activation: str | None = None
-
-
-@dataclass
-class ActorNetworkConfig:
-    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
-    activate_final: bool = True
-    use_layer_norm: bool = True
-
-
-@dataclass
-class NoiseActorConfig:
-    """Configuration for the noise actor in DSRL.
-    The noise actor outputs noise that gets fed to the diffusion policy.
-    """
-
-    use_tanh_squash: bool = False  # Whether to bound the noise output
-    std_min: float = 1e-5
-    std_max: float = 2.0
-    init_final: float = 0.05
-
-
-@PreTrainedConfig.register_subclass("dsrl")
-@dataclass
-class DSRLConfig(PreTrainedConfig):
-    """Diffusion Steering via Reinforcement Learning (DSRL) configuration."""
-
-    # Mapping of feature types to normalization modes
-    normalization_mapping: dict[str, NormalizationMode] = field(
-        default_factory=lambda: {
-            "VISUAL": NormalizationMode.MEAN_STD,
-            "STATE": NormalizationMode.MIN_MAX,
-            "ENV": NormalizationMode.MIN_MAX,
-            "ACTION": NormalizationMode.MIN_MAX,
-        }
-    )
-
-    # Statistics for normalizing different types of inputs
-    dataset_stats: dict[str, dict[str, list[float]]] | None = field(
-        default_factory=lambda: {
-            OBS_IMAGE: {
-                "mean": [0.485, 0.456, 0.406],
-                "std": [0.229, 0.224, 0.225],
-            },
-            OBS_STATE: {
-                "min": [0.0, 0.0],
-                "max": [1.0, 1.0],
-            },
-            ACTION: {
-                "min": [0.0, 0.0, 0.0],
-                "max": [1.0, 1.0, 1.0],
-            },
-        }
-    )
-
-    # Architecture specifics
-    # Device to run the model on (e.g., "cuda", "cpu")
-    device: str = "cpu"
-    # Device to store the model on
-    storage_device: str = "cpu"
-    # Name of the vision encoder model (Set to "helper2424/resnet10" for hil serl resnet10)
-    vision_encoder_name: str | None = None
-    # Whether to freeze the vision encoder during training
-    freeze_vision_encoder: bool = True
-    # Hidden dimension size for the image encoder
-    image_encoder_hidden_dim: int = 32
-    # Whether to use a shared encoder for actor and critic
-    shared_encoder: bool = True
-    # Number of discrete actions, eg for gripper actions
-    num_discrete_actions: int | None = None
-    # Dimension of the image embedding pooling
-    image_embedding_pooling_dim: int = 8
-
-    # Name of the action policy
-    action_policy_name: str = "pi0"
-    action_policy_weights: str | None = "lerobot/pi0_base"
-
-    # Training parameter
-    # Number of steps for online training
-    online_steps: int = 1000000
-    # Capacity of the online replay buffer
-    online_buffer_capacity: int = 100000
-    # Capacity of the offline replay buffer
-    offline_buffer_capacity: int = 100000
-    # Whether to use asynchronous prefetching for the buffers
-    async_prefetch: bool = False
-    # Number of steps before learning starts
-    online_step_before_learning: int = 100
-    # Frequency of policy updates
-    policy_update_freq: int = 1
-
-    # SAC algorithm parameters
-    discount: float = 0.99
-    # Initial temperature value
-    temperature_init: float = 1.0
-    # Number of critics in the ensemble
-    num_critics: int = 2
-    # Number of subsampled critics for training
-    num_subsample_critics: int | None = None
-    # Learning rate for the critic network
-    critic_lr: float = 3e-4
-    # Learning rate for the actor network
-    actor_lr: float = 3e-4
-    # Learning rate for the temperature parameter
-    temperature_lr: float = 3e-4
-    # Weight for the critic target update
-    critic_target_update_weight: float = 0.005
-    # Update-to-data ratio for the UTD algorithm (If you want enable utd_ratio, you need to set it to >1)
-    utd_ratio: int = 1
-    # Hidden dimension size for the state encoder
-    state_encoder_hidden_dim: int = 256
-    # Dimension of the latent space
-    latent_dim: int = 256
-    # Target entropy for the SAC algorithm
-    target_entropy: float | None = None
-    # Whether to use backup entropy for the SAC algorithm
-    use_backup_entropy: bool = True
-    # Gradient clipping norm for the SAC algorithm
-    grad_clip_norm: float = 40.0
-
-    # Network configuration
-    # Configuration for the critic network architecture
-    critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
-    # Configuration for the noise critic network architecture
-    noise_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
-    # Configuration for the noise actor network architecture
-    noise_actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
-    # Configuration for the noise actor specific parameters
-    noise_actor_kwargs: NoiseActorConfig = field(default_factory=NoiseActorConfig)
-    # Configuration for actor-learner architecture
-    actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig)
-    # Configuration for concurrency settings (you can use threads or processes for the actor and learner)
-    concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig)
-
-    # Optimizations
-    use_torch_compile: bool = True
-
-    def __post_init__(self):
-        super().__post_init__()
-
-    def get_optimizer_preset(self) -> MultiAdamConfig:
-        return MultiAdamConfig(
-            weight_decay=0.0,
-            optimizer_groups={
-                "critic_action": {"lr": self.critic_lr},
-                "critic_noise": {"lr": self.critic_lr},
-                "noise_actor": {"lr": self.actor_lr},
-                "temperature": {"lr": self.temperature_lr},
-            },
-        )
-
-    def get_scheduler_preset(self) -> None:
-        return None
-
-    def validate_features(self) -> None:
-        has_image = any(is_image_feature(key) for key in self.input_features)
-        has_state = OBS_STATE in self.input_features
-
-        if not (has_state or has_image):
-            raise ValueError(
-                "You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features"
-            )
-
-        if ACTION not in self.output_features:
-            raise ValueError("You must provide 'action' in the output features")
-
-    @property
-    def image_features(self) -> list[str]:
-        return [key for key in self.input_features if is_image_feature(key)]
-
-    @property
-    def observation_delta_indices(self) -> list:
-        return None
-
-    @property
-    def action_delta_indices(self) -> list:
-        return None
-
-    @property
-    def reward_delta_indices(self) -> None:
-        return None

src/lerobot/policies/dsrl/processor_dsrl.py

@@ -1,89 +0,0 @@
-# !/usr/bin/env python
-
-# Copyright 2025 The HuggingFace Inc. team.
-# All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Processor for DSRL policy.
-
-DSRL uses a similar processing pipeline as SAC since it operates on
-state-action transitions. The main difference is that internally it
-also works with noise, but that's handled within the policy itself.
-"""
-
-from typing import Any
-
-import torch
-
-from lerobot.policies.dsrl.configuration_dsrl import DSRLConfig
-from lerobot.processor import (
-    AddBatchDimensionProcessorStep,
-    DeviceProcessorStep,
-    NormalizerProcessorStep,
-    PolicyAction,
-    PolicyProcessorPipeline,
-    RenameObservationsProcessorStep,
-    UnnormalizerProcessorStep,
-)
-from lerobot.processor.converters import (
-    policy_action_to_transition,
-    transition_to_policy_action,
-)
-from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
-
-
-def make_dsrl_pre_post_processors(
-    config: DSRLConfig,
-    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
-) -> tuple[
-    PolicyProcessorPipeline[dict, dict],
-    PolicyProcessorPipeline[PolicyAction, PolicyAction],
-]:
-    """Create preprocessor and postprocessor pipelines for DSRL policy.
-
-    Args:
-        config: DSRL policy configuration
-        dataset_stats: Optional dataset statistics for normalization
-
-    Returns:
-        Tuple of (preprocessor, postprocessor) pipelines
-    """
-    input_steps = [
-        RenameObservationsProcessorStep(rename_map={}),
-        AddBatchDimensionProcessorStep(),
-        DeviceProcessorStep(device=config.device),
-        NormalizerProcessorStep(
-            features={**config.input_features, **config.output_features},
-            norm_map=config.normalization_mapping,
-            stats=dataset_stats,
-        ),
-    ]
-    output_steps = [
-        UnnormalizerProcessorStep(
-            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
-        ),
-        DeviceProcessorStep(device="cpu"),
-    ]
-    return (
-        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
-            steps=input_steps,
-            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
-        ),
-        PolicyProcessorPipeline[PolicyAction, PolicyAction](
-            steps=output_steps,
-            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
-            to_transition=policy_action_to_transition,
-            to_output=transition_to_policy_action,
-        ),
-    )

src/lerobot/policies/factory.py

@@ -30,7 +30,6 @@ from lerobot.envs.configs import EnvConfig
 from lerobot.envs.utils import env_to_policy_features
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
-from lerobot.policies.dsrl.configuration_dsrl import DSRLConfig
 from lerobot.policies.groot.configuration_groot import GrootConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
@@ -60,7 +59,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
 
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla", "dsrl".
+              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla".
 
     Returns:
         The policy class corresponding to the given name.
@@ -104,10 +103,6 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
-    elif name == "dsrl":
-        from lerobot.policies.dsrl.modeling_dsrl import DSRLPolicy
-
-        return DSRLPolicy
     elif name == "groot":
         from lerobot.policies.groot.modeling_groot import GrootPolicy
 
@@ -126,7 +121,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
     Args:
         policy_type: The type of the policy. Supported types include "tdmpc",
                      "diffusion", "act", "vqbet", "pi0", "pi05", "sac", "smolvla",
-                     "reward_classifier", "dsrl".
+                     "reward_classifier".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -153,8 +148,6 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return SmolVLAConfig(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
-    elif policy_type == "dsrl":
-        return DSRLConfig(**kwargs)
     elif policy_type == "groot":
         return GrootConfig(**kwargs)
     else:
@@ -328,21 +321,6 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
-    elif isinstance(policy_cfg, DSRLConfig):
-        from lerobot.policies.dsrl.processor_dsrl import make_dsrl_pre_post_processors
-
-        processors = make_dsrl_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
-
-    elif isinstance(policy_cfg, GrootConfig):
-        from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors
-
-        processors = make_groot_pre_post_processors(
-            config=policy_cfg,
-            dataset_stats=kwargs.get("dataset_stats"),
-        )
 
     elif isinstance(policy_cfg, GrootConfig):
         from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors

src/lerobot/policies/pi0/modeling_pi0.py

@@ -1148,7 +1148,7 @@ class PI0Policy(PreTrainedPolicy):
         return self._action_queue.popleft()
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
         """Predict a chunk of actions given environment observations."""
         self.eval()
 
@@ -1158,7 +1158,7 @@ class PI0Policy(PreTrainedPolicy):
         state = self.prepare_state(batch)
 
         # Sample actions using the model
-        actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state, noise)
+        actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state)
 
         # Unpad actions to actual action dimension
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/policies/pi05/modeling_pi05.py

@@ -1120,7 +1120,7 @@ class PI05Policy(PreTrainedPolicy):
         return self._action_queue.popleft()
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
         """Predict a chunk of actions given environment observations."""
         self.eval()
 
@@ -1129,7 +1129,7 @@ class PI05Policy(PreTrainedPolicy):
         tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
 
         # Sample actions using the model (no separate state needed for PI05)
-        actions = self.model.sample_actions(images, img_masks, tokens, masks, noise)
+        actions = self.model.sample_actions(images, img_masks, tokens, masks)
 
         # Unpad actions to actual action dimension
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -103,6 +103,7 @@ class SplitConfig:
 class MergeConfig:
     type: str = "merge"
     repo_ids: list[str] | None = None
+    num_workers: int | None = None
 
 
 @dataclass
@@ -215,6 +216,7 @@ def handle_merge(cfg: EditDatasetConfig) -> None:
         datasets,
         output_repo_id=cfg.repo_id,
         output_dir=output_dir,
+        num_workers=cfg.operation.num_workers,
     )
 
     logging.info(f"Merged dataset saved to {output_dir}")