Merge branch 'feat/add_relative_action_pi_models' into feat/mirror

* fix(edit dataset operation): fixing dataset tools CLI operation type specification

* test(edit dataset operation): adding tests for dataset tools operation type specification

* chore(format): running pre-commit

* chore(backward compatibility): adding a type property in OperationConfig for backward compatibility

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

benchmarks/tokens/README.md

@@ -1,134 +0,0 @@
-# Action tokenizer benchmark
-
-## Questions
-
-What is the trade-off between:
-
-- **Compression**: how many tokens are needed to represent an action chunk (e.g. horizon × action_dim floats)?
-- **Reconstruction quality**: how well does encode-then-decode preserve the original actions?
-- **Speed**: how long does encoding and decoding take per chunk?
-
-How to choose an action tokenizer?
-
-- Which tokenizer architecture (e.g. dct + BPE, DCT + BPE)?
-- Which **action horizon** and **encoded dimensions** to use?
-- Which **normalization** (QUANTILES, MEAN_STD, MIN_MAX) and **delta transform** (relative vs absolute actions)?
-- How do reconstruction error and compression ratio vary across datasets and tokenizer settings?
-
-This benchmark loads action chunks from a LeRobot dataset using the same pipeline as `lerobot-train-tokenizer`, runs a trained action tokenizer in encode/decode mode, and reports reconstruction error, compression stats, and timing. Results are saved as JSON under `outputs/` for comparison and analysis.
-
-## Variables
-
-**Dataset & chunking**
-
-- **repo_id**: LeRobot dataset (e.g. `lerobot/pusht`). Action statistics and normalization are taken from the dataset metadata when available.
-- **action_horizon**: Number of future steps per action chunk (must match the tokenizer’s training).
-- **encoded_dims**: Dimension ranges to encode (e.g. `0:6` or `0:6,7:14`). Must match the tokenizer.
-- **max_episodes**: Cap on episodes to load (default: all).
-- **sample_fraction**: Fraction of chunks to sample per episode (default `0.2`) to keep runtime manageable.
-
-**Transform & normalization**
-
-- **normalization_mode**: `IDENTITY`, `MEAN_STD`, `MIN_MAX`, `QUANTILES`, `QUANTILE10`. Should match the tokenizer’s training.
-- **delta_dims**: Comma-separated dimension indices for delta (relative) transform.
-- **use_delta_transform**: Whether to convert actions to relative to current state for those dimensions.
-- **state_key**: Dataset key for state (e.g. `observation.state`) used when applying delta transform.
-
-**Tokenizer & evaluation**
-
-- **action_tokenizer_path**: Path or HuggingFace repo id of the trained tokenizer (e.g. `outputs/wavetoken`).
-- **max_chunks_for_reconstruction**: Max number of chunks to use for reconstruction and timing (default `500`) to limit runtime.
-
-### Main parameters
-
-| parameter                        | default                      | description                                      |
-| -------------------------------- | ---------------------------- | ------------------------------------------------ |
-| **action_tokenizer_path**        | (required)                   | Path or Hub id of the trained action tokenizer.  |
-| **repo_id**                      | (required)                   | LeRobot dataset repo id.                         |
-| **action_horizon**               | `10`                         | Future steps per chunk.                          |
-| **encoded_dims**                 | `0:6`                        | Dimension ranges to encode (e.g. `0:6,7:14`).   |
-| **normalization_mode**           | `QUANTILES`                  | Normalization mode for actions.                  |
-| **max_episodes**                 | all                          | Max episodes to load.                            |
-| **sample_fraction**              | `0.2`                        | Fraction of chunks sampled per episode.          |
-| **max_chunks_for_reconstruction**| `500`                        | Chunks used for reconstruction and timing.       |
-| **output_dir**                   | `outputs/action_tokenizer_benchmark` | Directory for results JSON.              |
-
-## Metrics
-
-**Reconstruction (lower is better)**
-
-- **reconstruction_mae**: Mean absolute error between original and decoded action chunks.
-- **reconstruction_mse**: Mean squared error.
-- **reconstruction_rmse**: Root mean squared error.
-- **reconstruction_max_abs_error**: Maximum absolute error over all dimensions and samples.
-- **per_dimension_mae**: MAE per action dimension (list of length `action_dim`).
-
-**Compression**
-
-- **compression_ratio**: Ratio (action_horizon × action_dim) / mean number of tokens. Higher means more compression.
-- **mean_token_length**, **std_token_length**: Mean and standard deviation of token count per chunk.
-- **min_token_length**, **max_token_length**: Min and max token count.
-- **p50_token_length**, **p99_token_length**: 50th and 99th percentile token counts.
-
-**Timing (seconds per chunk)**
-
-- **mean_encode_time_sec**: Mean time to encode one chunk.
-- **mean_decode_time_sec**: Mean time to decode one chunk.
-
-The JSON output also includes **num_chunks_evaluated** and **total_chunks_available** for context.
-
-## How the benchmark works
-
-1. **Load dataset**: LeRobot dataset is loaded for the given `repo_id` and `root`.
-2. **Build action chunks**: For each episode (up to `max_episodes`), action chunks are built with the same logic as `lerobot-train-tokenizer`: sliding window of length `action_horizon`, optional delta transform, and per-episode sampling with `sample_fraction`.
-3. **Extract and normalize**: Only `encoded_dims` are kept. Normalization is applied using the dataset’s action stats when available, according to `normalization_mode`.
-4. **Encode / decode**: A random sample of chunks (size `max_chunks_for_reconstruction`) is encoded and then decoded with the tokenizer. Encode and decode times are recorded per chunk.
-5. **Compute metrics**: Reconstruction metrics are computed between original and decoded chunks; compression and timing stats are aggregated.
-6. **Save results**: A JSON file is written to `output_dir` with name `{timestamp}_{repo_id}_action_tokenizer_results.json`, containing the full config and all metrics.
-
-The pipeline (chunking, dimensions, normalization, delta) must match how the tokenizer was trained; otherwise reconstruction error can be large or the tokenizer may raise.
-
-## Caveats
-
-- The tokenizer’s **action_horizon** and **action_dim** (and optionally DCT settings) are fixed at training time. The benchmark infers dimensions from the dataset and encoded dims; the tokenizer path must correspond to a model trained with the same horizon and encoded dimensions.
-- Reconstruction is evaluated in **normalized space** (the same space the tokenizer sees). For interpretation in raw action space, you would need to invert normalization outside this script.
-- Only one tokenizer and one dataset are evaluated per run. To compare tokenizers or datasets, run the script multiple times and compare the saved JSON files.
-
-## Example
-
-Quick run with a local tokenizer and a small number of episodes:
-
-```bash
-python benchmarks/tokens/run_action_tokenizer_benchmark.py \
-    --action-tokenizer-path=outputs/wavetoken \
-    --repo-id=lerobot/pusht \
-    --action-horizon=10 \
-    --max-episodes=50 \
-    --output-dir=outputs/action_tokenizer_benchmark
-```
-
-With delta transform and custom encoded dimensions:
-
-```bash
-python benchmarks/tokens/run_action_tokenizer_benchmark.py \
-    --action-tokenizer-path=outputs/wavetoken \
-    --repo-id=lerobot/pusht \
-    --action-horizon=10 \
-    --encoded-dims=0:6,7:14 \
-    --delta-dims=0,1,2,3,4,5 \
-    --use-delta-transform \
-    --normalization-mode=QUANTILES \
-    --max-chunks-for-reconstruction=500 \
-    --output-dir=outputs/action_tokenizer_benchmark
-```
-
-Results are written to e.g. `outputs/action_tokenizer_benchmark/2026-02-12_14-30-00_lerobot_pusht_action_tokenizer_results.json`.
-
-## Results
-
-Results are stored as JSON in the directory given by `--output-dir` (default: `outputs/action_tokenizer_benchmark`). Each file contains:
-
-- **config**: All script arguments (tokenizer path, repo_id, action_horizon, encoded_dims, normalization_mode, etc.) for reproducibility.
-- **metrics**: All reconstruction, compression, and timing metrics described above.
-
-To compare runs, load and diff or aggregate these JSON files with your own scripts or notebooks.

benchmarks/tokens/run_action_tokenizer_benchmark.py

@@ -1,442 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2026 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.
-"""Benchmark action tokenization: reconstruction error, compression ratio, and timing.
-
-Loads action chunks from a LeRobot dataset, encodes/decodes them with a trained action
-tokenizer, and reports:
-- Reconstruction: MAE, MSE, RMSE, max absolute error, per-dimension MAE
-- Jerk: mean absolute jerk (original and reconstructed), jerk reconstruction MAE
-- Compression: ratio (input size / mean tokens), token length stats
-- Timing: mean encode/decode time per chunk
-
-Results are saved to outputs/action_tokenizer_benchmark/<timestamp>_results.json.
-
-Example:
-
-```bash
-python benchmarks/tokens/run_action_tokenizer_benchmark.py \
-    --action-tokenizer-path=outputs/wavetoken \
-    --repo-id=lerobot/pusht \
-    --action-horizon=10 \
-    --max-episodes=50 \
-    --output-dir=outputs/action_tokenizer_benchmark
-```
-"""
-
-import argparse
-import json
-import time
-from pathlib import Path
-
-import numpy as np
-
-from lerobot.configs.types import NormalizationMode
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.utils.constants import ACTION, OBS_STATE
-
-# Optional: use same helpers as train script if we want to avoid duplication
-from lerobot.scripts.lerobot_train_tokenizer import (
-    apply_normalization,
-    process_episode,
-)
-
-
-def load_action_chunks(
-    repo_id: str,
-    root: str | None,
-    action_horizon: int,
-    max_episodes: int | None,
-    sample_fraction: float,
-    encoded_dims: str,
-    delta_dims: str | None,
-    use_delta_transform: bool,
-    state_key: str,
-    normalization_mode: NormalizationMode,
-):
-    """Load and normalize action chunks from a LeRobot dataset (same pipeline as training)."""
-    dataset = LeRobotDataset(repo_id=repo_id, root=root)
-    num_episodes = dataset.num_episodes
-    if max_episodes is not None:
-        num_episodes = min(max_episodes, num_episodes)
-
-    # Parse encoded dims
-    encoded_dim_ranges = []
-    for range_str in encoded_dims.split(","):
-        start, end = map(int, range_str.strip().split(":"))
-        encoded_dim_ranges.append((start, end))
-    total_encoded_dims = sum(end - start for start, end in encoded_dim_ranges)
-
-    delta_dim_list = None
-    if delta_dims is not None and delta_dims.strip():
-        delta_dim_list = [int(d.strip()) for d in delta_dims.split(",")]
-
-    all_chunks = []
-    for ep_idx in range(num_episodes):
-        chunks = process_episode(
-            (
-                dataset,
-                ep_idx,
-                action_horizon,
-                delta_dim_list,
-                sample_fraction,
-                state_key,
-                use_delta_transform,
-            )
-        )
-        if chunks is not None:
-            all_chunks.append(chunks)
-
-    if not all_chunks:
-        raise ValueError("No action chunks collected. Check action_horizon and dataset.")
-
-    all_chunks = np.concatenate(all_chunks, axis=0)
-
-    # Extract encoded dimensions only
-    encoded_chunks = []
-    for start, end in encoded_dim_ranges:
-        encoded_chunks.append(all_chunks[:, :, start:end])
-    encoded_chunks = np.concatenate(encoded_chunks, axis=-1)
-
-    # Normalize
-    norm_stats = dataset.meta.stats
-    if norm_stats is not None and ACTION in norm_stats:
-        action_stats = norm_stats[ACTION]
-        encoded_dim_indices = []
-        for start, end in encoded_dim_ranges:
-            encoded_dim_indices.extend(range(start, end))
-        encoded_dim_indices = np.array(encoded_dim_indices)
-        encoded_stats = {}
-        for stat_name, stat_values in action_stats.items():
-            if isinstance(stat_values, (list, np.ndarray)):
-                stat_array = np.array(stat_values)
-                if len(stat_array) > max(encoded_dim_indices):
-                    encoded_stats[stat_name] = stat_array[encoded_dim_indices]
-        if encoded_stats:
-            try:
-                encoded_chunks = apply_normalization(
-                    encoded_chunks, encoded_stats, normalization_mode, eps=1e-8
-                )
-            except ValueError:
-                pass
-
-    return encoded_chunks, total_encoded_dims, action_horizon, dataset.repo_id
-
-
-def compute_reconstruction_metrics(original: np.ndarray, reconstructed: np.ndarray):
-    """Compute reconstruction error metrics (original and reconstructed same shape [N, T, D])."""
-    diff = reconstructed - original
-    mae = float(np.mean(np.abs(diff)))
-    mse = float(np.mean(diff**2))
-    rmse = float(np.sqrt(mse))
-    max_abs_err = float(np.max(np.abs(diff)))
-
-    # Per-dimension MAE (over N and T)
-    per_dim_mae = np.mean(np.abs(diff), axis=(0, 1))
-    per_dim_mae = per_dim_mae.tolist()
-
-    return {
-        "reconstruction_mae": mae,
-        "reconstruction_mse": mse,
-        "reconstruction_rmse": rmse,
-        "reconstruction_max_abs_error": max_abs_err,
-        "per_dimension_mae": per_dim_mae,
-    }
-
-
-def compute_jerk_metrics(original: np.ndarray, reconstructed: np.ndarray) -> dict:
-    """Compute jerk (3rd derivative of action w.r.t. time) metrics.
-
-    Args:
-        original: Action chunks [N, T, D].
-        reconstructed: Reconstructed action chunks [N, T, D].
-
-    Returns:
-        Dict with mean absolute jerk for original, reconstructed, and jerk reconstruction MAE.
-    """
-    # Jerk = 3rd discrete difference along time axis; need T >= 4
-    if original.shape[1] < 4:
-        return {}
-    jerk_orig = np.diff(original, n=3, axis=1)  # (N, T-3, D)
-    jerk_recon = np.diff(reconstructed, n=3, axis=1)
-    mae_jerk_orig = float(np.mean(np.abs(jerk_orig)))
-    mae_jerk_recon = float(np.mean(np.abs(jerk_recon)))
-    jerk_reconstruction_mae = float(np.mean(np.abs(jerk_recon - jerk_orig)))
-    return {
-        "jerk_mae_original": mae_jerk_orig,
-        "jerk_mae_reconstructed": mae_jerk_recon,
-        "jerk_reconstruction_mae": jerk_reconstruction_mae,
-    }
-
-
-def run_benchmark(
-    action_chunks: np.ndarray,
-    action_horizon: int,
-    action_dim: int,
-    tokenizer_path: str,
-    max_chunks_for_reconstruction: int | None = 500,
-):
-    """Encode/decode action chunks and compute metrics."""
-    from transformers import AutoProcessor
-
-    processor = AutoProcessor.from_pretrained(tokenizer_path, trust_remote_code=True)
-
-    n_chunks = len(action_chunks)
-    sample_size = n_chunks
-    if max_chunks_for_reconstruction is not None:
-        sample_size = min(max_chunks_for_reconstruction, n_chunks)
-    rng = np.random.RandomState(42)
-    indices = rng.choice(n_chunks, size=sample_size, replace=False)
-    sample_chunks = action_chunks[indices]
-
-    # Encode
-    token_lengths = []
-    encode_times = []
-    all_tokens = []
-    for i in range(len(sample_chunks)):
-        chunk = sample_chunks[i : i + 1]
-        t0 = time.perf_counter()
-        tokens = processor(chunk)[0]
-        encode_times.append(time.perf_counter() - t0)
-        if isinstance(tokens, list):
-            token_lengths.append(len(tokens))
-            all_tokens.append(tokens)
-        else:
-            n = tokens.shape[0] if hasattr(tokens, "shape") else len(tokens)
-            token_lengths.append(n)
-            all_tokens.append(tokens.tolist() if hasattr(tokens, "tolist") else list(tokens))
-
-    # Decode (processor keeps time_horizon/action_dim from encode)
-    decoded_list = []
-    decode_times = []
-    for i, tok_list in enumerate(all_tokens):
-        t0 = time.perf_counter()
-        recon = processor.decode(
-            [tok_list],
-            time_horizon=action_horizon,
-            action_dim=action_dim,
-        )
-        decode_times.append(time.perf_counter() - t0)
-        decoded_list.append(recon)
-    decoded = np.concatenate(decoded_list, axis=0)
-
-    # Reconstruction metrics
-    metrics = compute_reconstruction_metrics(sample_chunks, decoded)
-
-    # Jerk metrics (3rd derivative along time)
-    jerk_metrics = compute_jerk_metrics(sample_chunks, decoded)
-    metrics.update(jerk_metrics)
-
-    # Compression
-    token_lengths = np.array(token_lengths)
-    input_size = action_horizon * action_dim
-    compression_ratio = input_size / float(np.mean(token_lengths))
-    metrics["compression_ratio"] = compression_ratio
-    metrics["mean_token_length"] = float(np.mean(token_lengths))
-    metrics["std_token_length"] = float(np.std(token_lengths))
-    metrics["min_token_length"] = int(np.min(token_lengths))
-    metrics["max_token_length"] = int(np.max(token_lengths))
-    metrics["p50_token_length"] = float(np.percentile(token_lengths, 50))
-    metrics["p99_token_length"] = float(np.percentile(token_lengths, 99))
-
-    # Timing (seconds per chunk)
-    metrics["mean_encode_time_sec"] = float(np.mean(encode_times))
-    metrics["mean_decode_time_sec"] = float(np.mean(decode_times))
-    metrics["num_chunks_evaluated"] = sample_size
-    metrics["total_chunks_available"] = n_chunks
-
-    return metrics
-
-
-def main(
-    action_tokenizer_path: str,
-    repo_id: str,
-    root: str | None = None,
-    action_horizon: int = 10,
-    max_episodes: int | None = 100,
-    sample_fraction: float = 0.2,
-    encoded_dims: str = "0:6",
-    delta_dims: str | None = None,
-    use_delta_transform: bool = False,
-    state_key: str = OBS_STATE,
-    normalization_mode: str = "QUANTILES",
-    max_chunks_for_reconstruction: int | None = 500,
-    output_dir: str | None = None,
-):
-    if output_dir is None:
-        output_dir = "outputs/action_tokenizer_benchmark"
-    output_path = Path(output_dir)
-    output_path.mkdir(parents=True, exist_ok=True)
-
-    try:
-        norm_mode = NormalizationMode(normalization_mode)
-    except ValueError:
-        norm_mode = NormalizationMode.QUANTILES
-
-    print("Loading action chunks...")
-    encoded_chunks, action_dim, horizon, _ = load_action_chunks(
-        repo_id=repo_id,
-        root=root,
-        action_horizon=action_horizon,
-        max_episodes=max_episodes,
-        sample_fraction=sample_fraction,
-        encoded_dims=encoded_dims,
-        delta_dims=delta_dims,
-        use_delta_transform=use_delta_transform,
-        state_key=state_key,
-        normalization_mode=norm_mode,
-    )
-    print(f"Loaded {len(encoded_chunks)} chunks, shape {encoded_chunks.shape} (H={horizon}, D={action_dim})")
-
-    print("Running tokenizer benchmark...")
-    metrics = run_benchmark(
-        action_chunks=encoded_chunks,
-        action_horizon=horizon,
-        action_dim=action_dim,
-        tokenizer_path=action_tokenizer_path,
-        max_chunks_for_reconstruction=max_chunks_for_reconstruction,
-    )
-
-    # Attach config for reproducibility
-    results = {
-        "config": {
-            "action_tokenizer_path": action_tokenizer_path,
-            "repo_id": repo_id,
-            "action_horizon": action_horizon,
-            "max_episodes": max_episodes,
-            "sample_fraction": sample_fraction,
-            "encoded_dims": encoded_dims,
-            "delta_dims": delta_dims,
-            "use_delta_transform": use_delta_transform,
-            "state_key": state_key,
-            "normalization_mode": normalization_mode,
-        },
-        "metrics": metrics,
-    }
-
-    timestamp = time.strftime("%Y-%m-%d_%H-%M-%S")
-    safe_repo = repo_id.replace("/", "_")
-    out_file = output_path / f"{timestamp}_{safe_repo}_action_tokenizer_results.json"
-    with open(out_file, "w") as f:
-        json.dump(results, f, indent=2)
-
-    print(f"Results saved to {out_file}")
-    print("Metrics:")
-    for k, v in metrics.items():
-        if isinstance(v, list):
-            print(f"  {k}: (length {len(v)})")
-        else:
-            print(f"  {k}: {v}")
-
-    return results
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Benchmark action tokenization (reconstruction error, compression, timing)."
-    )
-    parser.add_argument(
-        "--action-tokenizer-path",
-        type=str,
-        required=True,
-        help="Path or HuggingFace repo id of the trained action tokenizer (e.g. outputs/wavetoken).",
-    )
-    parser.add_argument(
-        "--repo-id",
-        type=str,
-        required=True,
-        help="LeRobot dataset repo id (e.g. lerobot/pusht).",
-    )
-    parser.add_argument(
-        "--root",
-        type=str,
-        default=None,
-        help="Root directory for LeRobot datasets.",
-    )
-    parser.add_argument(
-        "--action-horizon",
-        type=int,
-        default=10,
-        help="Number of future steps per action chunk.",
-    )
-    parser.add_argument(
-        "--max-episodes",
-        type=int,
-        default=None,
-        help="Max episodes to use (default: all).",
-    )
-    parser.add_argument(
-        "--sample-fraction",
-        type=float,
-        default=0.2,
-        help="Fraction of chunks to sample per episode.",
-    )
-    parser.add_argument(
-        "--encoded-dims",
-        type=str,
-        default="0:6",
-        help="Dimension ranges to encode (e.g. 0:6,7:14).",
-    )
-    parser.add_argument(
-        "--delta-dims",
-        type=str,
-        default=None,
-        help="Comma-separated dimensions for delta transform.",
-    )
-    parser.add_argument(
-        "--use-delta-transform",
-        action="store_true",
-        help="Apply delta (relative) transform to specified dimensions.",
-    )
-    parser.add_argument(
-        "--state-key",
-        type=str,
-        default=OBS_STATE,
-        help="Dataset key for state (for delta transform).",
-    )
-    parser.add_argument(
-        "--normalization-mode",
-        type=str,
-        default="QUANTILES",
-        choices=[m.value for m in NormalizationMode],
-        help="Normalization mode for actions.",
-    )
-    parser.add_argument(
-        "--max-chunks-for-reconstruction",
-        type=int,
-        default=500,
-        help="Max chunks to use for reconstruction metrics (default: 500).",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default="outputs/action_tokenizer_benchmark",
-        help="Directory to save results JSON (default: outputs/action_tokenizer_benchmark).",
-    )
-    args = parser.parse_args()
-    main(
-        action_tokenizer_path=args.action_tokenizer_path,
-        repo_id=args.repo_id,
-        root=args.root,
-        action_horizon=args.action_horizon,
-        max_episodes=args.max_episodes,
-        sample_fraction=args.sample_fraction,
-        encoded_dims=args.encoded_dims,
-        delta_dims=args.delta_dims,
-        use_delta_transform=args.use_delta_transform,
-        state_key=args.state_key,
-        normalization_mode=args.normalization_mode,
-        max_chunks_for_reconstruction=args.max_chunks_for_reconstruction,
-        output_dir=args.output_dir,
-    )

src/lerobot/datasets/dataset_tools.py

@@ -37,7 +37,7 @@ import torch
 from tqdm import tqdm
 
 from lerobot.datasets.aggregate import aggregate_datasets
-from lerobot.datasets.compute_stats import aggregate_stats
+from lerobot.datasets.compute_stats import aggregate_stats, compute_episode_stats, get_feature_stats
 from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
 from lerobot.datasets.utils import (
     DATA_DIR,
@@ -1522,6 +1522,122 @@ def modify_tasks(
     return dataset
 
 
+def recompute_stats(
+    dataset: LeRobotDataset,
+    skip_image_video: bool = True,
+    delta_action: bool = False,
+    delta_exclude_joints: list[str] | None = None,
+) -> LeRobotDataset:
+    """Recompute stats.json from scratch by iterating all episodes.
+
+    Args:
+        dataset: The LeRobotDataset to recompute stats for.
+        skip_image_video: If True (default), only recompute stats for numeric features
+            (action, state, etc.) and keep existing image/video stats unchanged.
+        delta_action: If True, compute action stats as delta (action - state).
+            Useful when training with use_delta_actions=True so normalization matches.
+        delta_exclude_joints: Joint names to exclude from delta conversion when
+            delta_action=True. These dims keep absolute stats. Uses dataset's
+            action feature names to build the mask. Default: ["gripper"].
+
+    Returns:
+        The same dataset with updated stats.
+    """
+    features = dataset.meta.features
+    numeric_features = {
+        k: v for k, v in features.items()
+        if v["dtype"] not in ["image", "video", "string"]
+        and k not in ["index", "episode_index", "task_index", "frame_index", "timestamp"]
+    }
+
+    if skip_image_video:
+        features_to_compute = numeric_features
+    else:
+        features_to_compute = {
+            k: v for k, v in features.items()
+            if v["dtype"] != "string"
+            and k not in ["index", "episode_index", "task_index", "frame_index", "timestamp"]
+        }
+
+    # Build delta mask if delta_action is enabled
+    delta_mask = None
+    if delta_action and "action" in features and "observation.state" in features:
+        if delta_exclude_joints is None:
+            delta_exclude_joints = ["gripper"]
+        action_names = features["action"].get("names")
+        if action_names is not None:
+            exclude = set(delta_exclude_joints)
+            delta_mask = [n not in exclude for n in action_names]
+        else:
+            action_dim = features["action"]["shape"][0]
+            delta_mask = [True] * action_dim
+        # Only recompute action stats when delta is enabled — state stays unchanged
+        features_to_compute = {"action": features["action"]}
+        logging.info(f"Recomputing action stats as delta (exclude: {delta_exclude_joints})")
+    else:
+        logging.info(f"Recomputing stats for features: {list(features_to_compute.keys())}")
+
+    data_dir = dataset.root / DATA_DIR
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    if not parquet_files:
+        raise ValueError(f"No parquet files found in {data_dir}")
+
+    all_episode_stats = []
+    numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
+    # Also need state for delta computation even though we don't recompute state stats
+    needs_state = delta_mask is not None
+
+    for parquet_path in tqdm(parquet_files, desc="Computing stats from data files"):
+        df = pd.read_parquet(parquet_path)
+
+        for ep_idx in sorted(df["episode_index"].unique()):
+            ep_df = df[df["episode_index"] == ep_idx]
+            episode_data = {}
+            for key in numeric_keys:
+                if key in ep_df.columns:
+                    values = ep_df[key].values
+                    if hasattr(values[0], "__len__"):
+                        episode_data[key] = np.stack(values)
+                    else:
+                        episode_data[key] = np.array(values)
+
+            # Apply delta conversion to actions before computing stats
+            if delta_mask is not None and "action" in episode_data:
+                from lerobot.processor.delta_action_processor import to_delta_actions
+
+                # Load state for delta even if we're not computing state stats
+                if needs_state and "observation.state" in ep_df.columns:
+                    state_values = ep_df["observation.state"].values
+                    if hasattr(state_values[0], "__len__"):
+                        states = np.stack(state_values)
+                    else:
+                        states = np.array(state_values)
+                    actions_t = torch.from_numpy(episode_data["action"]).float()
+                    states_t = torch.from_numpy(states).float()
+                    episode_data["action"] = to_delta_actions(actions_t, states_t, delta_mask).numpy()
+
+            ep_stats = compute_episode_stats(episode_data, features_to_compute)
+            all_episode_stats.append(ep_stats)
+
+    if not all_episode_stats:
+        logging.warning("No episode stats computed")
+        return dataset
+
+    new_stats = aggregate_stats(all_episode_stats)
+
+    # Merge: keep existing stats for features we didn't recompute
+    if dataset.meta.stats:
+        for key, value in dataset.meta.stats.items():
+            if key not in new_stats:
+                new_stats[key] = value
+
+    write_stats(new_stats, dataset.root)
+    dataset.meta.stats = new_stats
+
+    logging.info(f"Stats recomputed for {len(all_episode_stats)} episodes")
+    return dataset
+
+
 def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
     output_dir: Path,

src/lerobot/policies/factory.py

@@ -470,6 +470,13 @@ def make_policy(
     cfg.output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
     if not cfg.input_features:
         cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
+
+    # Store action feature names for delta_exclude_joints support
+    if ds_meta is not None and hasattr(cfg, "action_feature_names"):
+        action_names = ds_meta.features.get(ACTION, {}).get("names")
+        if action_names is not None:
+            cfg.action_feature_names = list(action_names)
+
     kwargs["config"] = cfg
 
     # Pass dataset_stats to the policy if available (needed for some policies like SARM)

src/lerobot/policies/pi0/configuration_pi0.py

@@ -50,6 +50,13 @@ class PI0Config(PreTrainedConfig):
     min_period: float = 4e-3
     max_period: float = 4.0
 
+    # Delta actions: converts absolute actions to delta (relative to state).
+    use_delta_actions: bool = False
+    # Joint names to exclude from delta (kept absolute). Empty list = all dims delta.
+    delta_exclude_joints: list[str] = field(default_factory=lambda: ["gripper"])
+    # Populated at runtime from dataset metadata by make_policy.
+    action_feature_names: list[str] | None = None
+
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi0/processor_pi0.py

@@ -21,8 +21,10 @@ import torch
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.processor import (
+    AbsoluteActionsProcessorStep,
     AddBatchDimensionProcessorStep,
     ComplementaryDataProcessorStep,
+    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -126,7 +128,13 @@ def make_pi0_pre_post_processors(
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
 
-    # Add remaining processors
+    delta_step = DeltaActionsProcessorStep(
+        enabled=config.use_delta_actions,
+        exclude_joints=getattr(config, "delta_exclude_joints", []),
+        action_names=getattr(config, "action_feature_names", None),
+    )
+
+    # OpenPI order: raw → delta → normalize → model → unnormalize → absolute
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
         AddBatchDimensionProcessorStep(),
@@ -138,6 +146,7 @@ def make_pi0_pre_post_processors(
             padding="max_length",
         ),
         DeviceProcessorStep(device=config.device),
+        delta_step,
         NormalizerProcessorStep(
             features={**config.input_features, **config.output_features},
             norm_map=config.normalization_mapping,
@@ -149,6 +158,7 @@ def make_pi0_pre_post_processors(
         UnnormalizerProcessorStep(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
+        AbsoluteActionsProcessorStep(enabled=config.use_delta_actions, delta_step=delta_step),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pi05/configuration_pi05.py

@@ -50,6 +50,13 @@ class PI05Config(PreTrainedConfig):
     min_period: float = 4e-3
     max_period: float = 4.0
 
+    # Delta actions: converts absolute actions to delta (relative to state).
+    use_delta_actions: bool = False
+    # Joint names to exclude from delta (kept absolute). Empty list = all dims delta.
+    delta_exclude_joints: list[str] = field(default_factory=lambda: ["gripper"])
+    # Populated at runtime from dataset metadata by make_policy.
+    action_feature_names: list[str] | None = None
+
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi05/processor_pi05.py

@@ -25,7 +25,9 @@ from lerobot.configs.types import PipelineFeatureType, PolicyFeature
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pi05.modeling_pi05 import pad_vector
 from lerobot.processor import (
+    AbsoluteActionsProcessorStep,
     AddBatchDimensionProcessorStep,
+    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -129,10 +131,19 @@ def make_pi05_pre_post_processors(
         A tuple containing the configured pre-processor and post-processor pipelines.
     """
 
-    # Add remaining processors
+    delta_step = DeltaActionsProcessorStep(
+        enabled=config.use_delta_actions,
+        exclude_joints=getattr(config, "delta_exclude_joints", []),
+        action_names=getattr(config, "action_feature_names", None),
+    )
+
+    # OpenPI order: raw → delta → normalize → model → unnormalize → absolute
+    # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
+    # because the tokenizer step expects normalized state in [-1, 1] range for discretization
     input_steps: list[ProcessorStep] = [
         RenameObservationsProcessorStep(rename_map={}),  # To mimic the same processor as pretrained one
         AddBatchDimensionProcessorStep(),
+        delta_step,
         # NOTE: NormalizerProcessorStep MUST come before Pi05PrepareStateTokenizerProcessorStep
         # because the tokenizer step expects normalized state in [-1, 1] range for discretization
         NormalizerProcessorStep(
@@ -154,6 +165,7 @@ def make_pi05_pre_post_processors(
         UnnormalizerProcessorStep(
             features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
         ),
+        AbsoluteActionsProcessorStep(enabled=config.use_delta_actions, delta_step=delta_step),
         DeviceProcessorStep(device="cpu"),
     ]
 

src/lerobot/policies/pi0_fast/configuration_pi0_fast.py

@@ -41,6 +41,9 @@ class PI0FastConfig(PreTrainedConfig):
     max_action_dim: int = 32
     max_action_tokens: int = 256
 
+    # Delta actions: converts absolute actions to delta (relative to state).
+    use_delta_actions: bool = False
+
     # Real-Time Chunking (RTC) configuration
     rtc_config: RTCConfig | None = None
 

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -48,12 +48,14 @@ from lerobot.configs.policies import PreTrainedConfig
 from lerobot.policies.pi0_fast.configuration_pi0_fast import PI0FastConfig
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
+from lerobot.processor.delta_action_processor import to_absolute_actions
 from lerobot.utils.constants import (
     ACTION,
     ACTION_TOKEN_MASK,
     ACTION_TOKENS,
     OBS_LANGUAGE_ATTENTION_MASK,
     OBS_LANGUAGE_TOKENS,
+    OBS_STATE,
     OPENPI_ATTENTION_MASK_VALUE,
 )
 
@@ -1315,6 +1317,12 @@ class PI0FastPolicy(PreTrainedPolicy):
             action_tokens, action_horizon=action_horizon, action_dim=action_dim
         )
 
+        if self.config.use_delta_actions and OBS_STATE in batch:
+            state = pad_vector(batch[OBS_STATE], self.config.max_state_dim)
+            continuous_actions = to_absolute_actions(
+                continuous_actions, state, [True] * continuous_actions.shape[-1]
+        )
+
         return continuous_actions
 
     def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:

src/lerobot/policies/pi0_fast/processor_pi0_fast.py

@@ -27,6 +27,7 @@ from lerobot.policies.pi0_fast.modeling_pi0_fast import pad_vector
 from lerobot.processor import (
     ActionTokenizerProcessorStep,
     AddBatchDimensionProcessorStep,
+    DeltaActionsProcessorStep,
     DeviceProcessorStep,
     NormalizerProcessorStep,
     PolicyAction,
@@ -147,6 +148,7 @@ def make_pi0_fast_pre_post_processors(
             padding_side="right",
             padding="max_length",
         ),
+        DeltaActionsProcessorStep(enabled=config.use_delta_actions),
         ActionTokenizerProcessorStep(
             action_tokenizer_name=config.action_tokenizer_name,
             max_action_tokens=config.max_action_tokens,

src/lerobot/processor/__init__.py

@@ -28,7 +28,14 @@ from .core import (
     RobotObservation,
     TransitionKey,
 )
-from .delta_action_processor import MapDeltaActionToRobotActionStep, MapTensorToDeltaActionDictStep
+from .delta_action_processor import (
+    AbsoluteActionsProcessorStep,
+    DeltaActionsProcessorStep,
+    MapDeltaActionToRobotActionStep,
+    MapTensorToDeltaActionDictStep,
+    to_absolute_actions,
+    to_delta_actions,
+)
 from .device_processor import DeviceProcessorStep
 from .factory import (
     make_default_processors,
@@ -97,6 +104,8 @@ __all__ = [
     "make_default_teleop_action_processor",
     "make_default_robot_action_processor",
     "make_default_robot_observation_processor",
+    "AbsoluteActionsProcessorStep",
+    "DeltaActionsProcessorStep",
     "MapDeltaActionToRobotActionStep",
     "MapTensorToDeltaActionDictStep",
     "NormalizerProcessorStep",
@@ -126,6 +135,8 @@ __all__ = [
     "transition_to_batch",
     "TransitionKey",
     "TruncatedProcessorStep",
+    "to_absolute_actions",
+    "to_delta_actions",
     "UnnormalizerProcessorStep",
     "VanillaObservationProcessorStep",
 ]

src/lerobot/processor/delta_action_processor.py

@@ -14,12 +14,54 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from collections.abc import Sequence
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+from torch import Tensor
 
 from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.utils.constants import OBS_STATE
 
-from .core import PolicyAction, RobotAction
-from .pipeline import ActionProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
+from .core import EnvTransition, PolicyAction, RobotAction, TransitionKey
+from .pipeline import ActionProcessorStep, ProcessorStep, ProcessorStepRegistry, RobotActionProcessorStep
+
+
+def to_delta_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) -> Tensor:
+    """Convert absolute actions to delta: delta = action - state (for masked dims).
+
+    Args:
+        actions: (B, T, action_dim) or (B, action_dim).
+        state: (B, state_dim). Broadcast across time dimension.
+        mask: Which dims to convert. Can be shorter than action_dim.
+    """
+    mask_t = torch.tensor(mask, dtype=actions.dtype, device=actions.device)
+    dims = mask_t.shape[0]
+    state_offset = state[..., :dims] * mask_t
+    if actions.ndim == 3:
+        state_offset = state_offset.unsqueeze(-2)
+    actions = actions.clone()
+    actions[..., :dims] -= state_offset
+    return actions
+
+
+def to_absolute_actions(actions: Tensor, state: Tensor, mask: Sequence[bool]) -> Tensor:
+    """Convert delta actions back to absolute: absolute = delta + state (for masked dims).
+
+    Args:
+        actions: (B, T, action_dim) or (B, action_dim).
+        state: (B, state_dim). Broadcast across time dimension.
+        mask: Which dims to convert. Can be shorter than action_dim.
+    """
+    mask_t = torch.tensor(mask, dtype=actions.dtype, device=actions.device)
+    dims = mask_t.shape[0]
+    state_offset = state[..., :dims] * mask_t
+    if actions.ndim == 3:
+        state_offset = state_offset.unsqueeze(-2)
+    actions = actions.clone()
+    actions[..., :dims] += state_offset
+    return actions
 
 
 @ProcessorStepRegistry.register("map_tensor_to_delta_action_dict")
@@ -141,3 +183,126 @@ class MapDeltaActionToRobotActionStep(RobotActionProcessorStep):
             )
 
         return features
+
+
+@ProcessorStepRegistry.register("delta_actions_processor")
+@dataclass
+class DeltaActionsProcessorStep(ProcessorStep):
+    """Converts absolute actions to delta actions (action -= state) for masked dimensions.
+
+    Mirrors OpenPI's DeltaActions transform. Applied during preprocessing so the model
+    trains on relative offsets instead of absolute positions.
+    Caches the last seen state so a paired AbsoluteActionsProcessorStep can reverse
+    the conversion during postprocessing.
+
+    Attributes:
+        enabled: Whether to apply the delta conversion.
+        exclude_joints: Joint names to keep absolute (not converted to delta).
+        action_names: Action dimension names from dataset metadata, used to build
+            the mask from exclude_joints. If None, all dims are converted.
+    """
+
+    enabled: bool = False
+    exclude_joints: list[str] = field(default_factory=list)
+    action_names: list[str] | None = None
+    _last_state: torch.Tensor | None = field(default=None, init=False, repr=False)
+
+    def _build_mask(self, action_dim: int) -> list[bool]:
+        if not self.exclude_joints or self.action_names is None:
+            return [True] * action_dim
+
+        exclude_tokens = [str(name).lower() for name in self.exclude_joints if name]
+        if not exclude_tokens:
+            return [True] * action_dim
+
+        mask = []
+        for name in self.action_names[:action_dim]:
+            action_name = str(name).lower()
+            is_excluded = any(token == action_name or token in action_name for token in exclude_tokens)
+            mask.append(not is_excluded)
+
+        if len(mask) < action_dim:
+            mask.extend([True] * (action_dim - len(mask)))
+
+        return mask
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        observation = transition.get(TransitionKey.OBSERVATION, {})
+        state = observation.get(OBS_STATE) if observation else None
+
+        # Always cache state for the paired AbsoluteActionsProcessorStep
+        if state is not None:
+            self._last_state = state
+
+        if not self.enabled:
+            return transition
+
+        new_transition = transition.copy()
+        action = new_transition.get(TransitionKey.ACTION)
+        if action is None or state is None:
+            return new_transition
+
+        mask = self._build_mask(action.shape[-1])
+        new_transition[TransitionKey.ACTION] = to_delta_actions(action, state, mask)
+        return new_transition
+
+    def get_config(self) -> dict[str, Any]:
+        return {"enabled": self.enabled, "exclude_joints": self.exclude_joints}
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
+
+
+@ProcessorStepRegistry.register("absolute_actions_processor")
+@dataclass
+class AbsoluteActionsProcessorStep(ProcessorStep):
+    """Converts delta actions back to absolute actions (action += state) for all dimensions.
+
+    Mirrors OpenPI's AbsoluteActions transform. Applied during postprocessing so
+    predicted deltas are converted back to absolute positions for execution.
+    Reads the cached state from its paired DeltaActionsProcessorStep.
+
+    Attributes:
+        enabled: Whether to apply the absolute conversion.
+        delta_step: Reference to the paired DeltaActionsProcessorStep that caches state.
+    """
+
+    enabled: bool = False
+    delta_step: DeltaActionsProcessorStep | None = field(default=None, repr=False)
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        if not self.enabled:
+            return transition
+
+        if self.delta_step is None:
+            raise RuntimeError(
+                "AbsoluteActionsProcessorStep requires a paired DeltaActionsProcessorStep "
+                "but delta_step is None. Ensure delta_step is set when constructing the postprocessor."
+            )
+
+        if self.delta_step._last_state is None:
+            raise RuntimeError(
+                "AbsoluteActionsProcessorStep requires state from DeltaActionsProcessorStep "
+                "but no state has been cached. Ensure the preprocessor runs before the postprocessor."
+            )
+
+        new_transition = transition.copy()
+        action = new_transition.get(TransitionKey.ACTION)
+        if action is None:
+            return new_transition
+
+        mask = self.delta_step._build_mask(action.shape[-1])
+        new_transition[TransitionKey.ACTION] = to_absolute_actions(
+            action, self.delta_step._last_state, mask
+        )
+        return new_transition
+
+    def get_config(self) -> dict[str, Any]:
+        return {"enabled": self.enabled}
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features

src/lerobot/processor/normalize_processor.py

@@ -331,11 +331,9 @@ class _NormalizationMixin:
                 )
 
             mean, std = stats["mean"], stats["std"]
-            # Avoid division by zero by adding a small epsilon.
-            denom = std + self.eps
             if inverse:
-                return tensor * std + mean
-            return (tensor - mean) / denom
+                return tensor * (std + 1e-6) + mean
+            return (tensor - mean) / (std + 1e-6)
 
         if norm_mode == NormalizationMode.MIN_MAX:
             min_val = stats.get("min", None)
@@ -367,11 +365,7 @@ class _NormalizationMixin:
                     "QUANTILES normalization mode requires q01 and q99 stats, please update the dataset with the correct stats using the `augment_dataset_quantile_stats.py` script"
                 )
 
-            denom = q99 - q01
-            # Avoid division by zero by adding epsilon when quantiles are identical
-            denom = torch.where(
-                denom == 0, torch.tensor(self.eps, device=tensor.device, dtype=tensor.dtype), denom
-            )
+            denom = q99 - q01 + 1e-6
             if inverse:
                 return (tensor + 1.0) * denom / 2.0 + q01
             return 2.0 * (tensor - q01) / denom - 1.0

src/lerobot/processor/pipeline.py

@@ -413,7 +413,7 @@ class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
         Args:
             save_directory: The directory where the pipeline will be saved. If None, saves to
                 HF_LEROBOT_HOME/processors/{sanitized_pipeline_name}.
-            repo_id: ID of your repository on the Hub. Used only if `push_to_hub=True`.
+            repo_id: ID of your repository on the Hub. Used only if `push_to_hub=true`.
             push_to_hub: Whether or not to push your object to the Hugging Face Hub after saving it.
             card_kwargs: Additional arguments passed to the card template to customize the card.
             config_filename: The name of the JSON configuration file. If None, a name is

src/lerobot/robots/so_follower/config_so_follower.py

@@ -40,7 +40,7 @@ class SOFollowerConfig:
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 
     # Set to `True` for backward compatibility with previous policies/dataset
-    use_degrees: bool = False
+    use_degrees: bool = True
 
 
 @RobotConfig.register_subclass("so101_follower")

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -109,11 +109,14 @@ Using JSON config file:
         --config_path path/to/edit_config.json
 """
 
+import abc
 import logging
 import shutil
 from dataclasses import dataclass
 from pathlib import Path
 
+import draccus
+
 from lerobot.configs import parser
 from lerobot.datasets.dataset_tools import (
     convert_image_to_video_dataset,
@@ -129,39 +132,46 @@ from lerobot.utils.utils import init_logging
 
 
 @dataclass
-class DeleteEpisodesConfig:
-    type: str = "delete_episodes"
+class OperationConfig(draccus.ChoiceRegistry, abc.ABC):
+    @property
+    def type(self) -> str:
+        return self.get_choice_name(self.__class__)
+
+
+@OperationConfig.register_subclass("delete_episodes")
+@dataclass
+class DeleteEpisodesConfig(OperationConfig):
     episode_indices: list[int] | None = None
 
 
+@OperationConfig.register_subclass("split")
 @dataclass
-class SplitConfig:
-    type: str = "split"
+class SplitConfig(OperationConfig):
     splits: dict[str, float | list[int]] | None = None
 
 
+@OperationConfig.register_subclass("merge")
 @dataclass
-class MergeConfig:
-    type: str = "merge"
+class MergeConfig(OperationConfig):
     repo_ids: list[str] | None = None
 
 
+@OperationConfig.register_subclass("remove_feature")
 @dataclass
-class RemoveFeatureConfig:
-    type: str = "remove_feature"
+class RemoveFeatureConfig(OperationConfig):
     feature_names: list[str] | None = None
 
 
+@OperationConfig.register_subclass("modify_tasks")
 @dataclass
-class ModifyTasksConfig:
-    type: str = "modify_tasks"
+class ModifyTasksConfig(OperationConfig):
     new_task: str | None = None
     episode_tasks: dict[str, str] | None = None
 
 
+@OperationConfig.register_subclass("convert_image_to_video")
 @dataclass
-class ConvertImageToVideoConfig:
-    type: str = "convert_image_to_video"
+class ConvertImageToVideoConfig(OperationConfig):
     output_dir: str | None = None
     vcodec: str = "libsvtav1"
     pix_fmt: str = "yuv420p"
@@ -177,14 +187,7 @@ class ConvertImageToVideoConfig:
 @dataclass
 class EditDatasetConfig:
     repo_id: str
-    operation: (
-        DeleteEpisodesConfig
-        | SplitConfig
-        | MergeConfig
-        | RemoveFeatureConfig
-        | ModifyTasksConfig
-        | ConvertImageToVideoConfig
-    )
+    operation: OperationConfig
     root: str | None = None
     new_repo_id: str | None = None
     push_to_hub: bool = False
@@ -450,10 +453,8 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
     elif operation_type == "convert_image_to_video":
         handle_convert_image_to_video(cfg)
     else:
-        raise ValueError(
-            f"Unknown operation type: {operation_type}\n"
-            f"Available operations: delete_episodes, split, merge, remove_feature, modify_tasks, convert_image_to_video"
-        )
+        available = ", ".join(OperationConfig.get_known_choices())
+        raise ValueError(f"Unknown operation: {operation_type}\nAvailable operations: {available}")
 
 
 def main() -> None:

src/lerobot/scripts/lerobot_mirror_dataset.py

@@ -0,0 +1,366 @@
+# 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.
+
+"""
+Mirror a bimanual robot dataset by swapping left/right arms and inverting joint values.
+
+This script creates a mirrored version of a dataset where:
+1. Left and right arm observations/actions are swapped
+2. Joint values are inverted according to a mirroring mask
+3. Video frames are horizontally flipped
+
+Example usage:
+```shell
+python -m lerobot.scripts.lerobot_mirror_dataset \
+    --repo_id=pepijn/openarm_bimanual \
+    --output_repo_id=pepijn/openarm_bimanual_mirrored
+```
+"""
+
+import argparse
+import logging
+import os
+import subprocess
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import (
+    DATA_DIR,
+    DEFAULT_DATA_PATH,
+    write_info,
+    write_stats,
+    write_tasks,
+)
+from lerobot.utils.constants import HF_LEROBOT_HOME
+
+logger = logging.getLogger(__name__)
+
+OPENARM_MIRRORING_MASK = {
+    "joint_1": -1,  # Pan - invert
+    "joint_2": -1,  # Lift - invert
+    "joint_3": -1,  # Roll - invert
+    "joint_4": 1,   # Elbow - no invert
+    "joint_5": -1,  # W-Roll - invert
+    "joint_6": -1,  # W-Pitch - invert
+    "joint_7": -1,  # W-Yaw - invert
+    "gripper": 1,   # Gripper - no invert
+}
+
+
+def get_mirroring_mask(robot_type: str) -> dict[str, int]:
+    """Get the mirroring mask for a given robot type."""
+    if robot_type in ["bi_openarm_follower", "openarm_follower", "bi_openarms_follower", "openarms_follower"]:
+        return OPENARM_MIRRORING_MASK
+    raise ValueError(f"Unknown robot type: {robot_type}. Add a mirroring mask for this robot.")
+
+
+def swap_left_right_name(name: str) -> str:
+    """Swap 'left' and 'right' in a feature name."""
+    # Use placeholder to avoid double-swap
+    result = name.replace("left_", "LEFT_PLACEHOLDER_")
+    result = result.replace("right_", "left_")
+    result = result.replace("LEFT_PLACEHOLDER_", "right_")
+    return result
+
+
+def mirror_feature_names(names: list[str]) -> tuple[list[str], dict[int, int]]:
+    """Mirror feature names by swapping left/right and return the new names and index mapping."""
+    mirrored_names = [swap_left_right_name(n) for n in names]
+    old_to_new_idx = {}
+    for old_idx, old_name in enumerate(names):
+        new_name = swap_left_right_name(old_name)
+        new_idx = mirrored_names.index(new_name)
+        old_to_new_idx[old_idx] = new_idx
+    return mirrored_names, old_to_new_idx
+
+
+def apply_mirroring_mask(
+    value: float,
+    feature_name: str,
+    mirroring_mask: dict[str, int],
+) -> float:
+    """Apply mirroring mask to a joint value."""
+    name_without_prefix = feature_name.split("_", 1)[1] if "_" in feature_name else feature_name
+    joint_name = name_without_prefix.split(".")[0]
+    if joint_name in mirroring_mask:
+        return value * mirroring_mask[joint_name]
+    return value
+
+
+def mirror_array(
+    array: np.ndarray,
+    names: list[str],
+    mirroring_mask: dict[str, int],
+) -> np.ndarray:
+    """Mirror an array of values (action or state) by swapping left/right and applying mask."""
+    mirrored_names, idx_mapping = mirror_feature_names(names)
+    result = np.zeros_like(array)
+    for old_idx, new_idx in idx_mapping.items():
+        old_name = names[old_idx]
+        new_name = mirrored_names[new_idx]
+        value = array[old_idx]
+        mirrored_value = apply_mirroring_mask(value, new_name, mirroring_mask)
+        result[new_idx] = mirrored_value
+    return result
+
+
+def flip_video_frames(
+    input_path: Path,
+    output_path: Path,
+    fps: float,
+    vcodec: str = "libsvtav1",
+):
+    """Flip video frames horizontally using FFmpeg with same settings as encode_video_frames."""
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    cmd = [
+        "ffmpeg", "-y", "-i", str(input_path),
+        "-vf", "hflip",
+        "-c:v", vcodec,
+        "-g", "2",
+        "-crf", "30",
+        "-r", str(int(fps)),
+        "-pix_fmt", "yuv420p",
+        "-loglevel", "error",
+    ]
+    if vcodec == "libsvtav1":
+        cmd.extend(["-preset", "12"])
+    cmd.append(str(output_path))
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"FFmpeg failed: {result.stderr}")
+
+
+def mirror_dataset(
+    repo_id: str,
+    output_repo_id: str,
+    root: str | Path | None = None,
+    output_root: str | Path | None = None,
+    mirroring_mask: dict[str, int] | None = None,
+    vcodec: str = "libsvtav1",
+    num_workers: int | None = None,
+) -> LeRobotDataset:
+    """Mirror a bimanual robot dataset."""
+    logger.info(f"Loading dataset: {repo_id}")
+    dataset = LeRobotDataset(repo_id, root=root)
+
+    if mirroring_mask is None:
+        robot_type = dataset.meta.robot_type or "bi_openarms_follower"
+        mirroring_mask = get_mirroring_mask(robot_type)
+        logger.info(f"Using mirroring mask for robot type: {robot_type}")
+
+    output_root = Path(output_root) if output_root else HF_LEROBOT_HOME / output_repo_id
+
+    mirrored_features = {}
+    for key, feat in dataset.meta.features.items():
+        new_key = swap_left_right_name(key)
+        new_feat = feat.copy()
+        if "names" in new_feat and new_feat["names"]:
+            new_feat["names"] = [swap_left_right_name(n) for n in new_feat["names"]]
+        mirrored_features[new_key] = new_feat
+
+    logger.info("Creating mirrored dataset metadata...")
+    new_meta = LeRobotDatasetMetadata.create(
+        repo_id=output_repo_id,
+        fps=dataset.meta.fps,
+        features=mirrored_features,
+        robot_type=dataset.meta.robot_type,
+        root=output_root,
+        use_videos=len(dataset.meta.video_keys) > 0,
+    )
+
+    if dataset.meta.tasks is not None:
+        write_tasks(dataset.meta.tasks, new_meta.root)
+        new_meta.tasks = dataset.meta.tasks.copy()
+
+    _mirror_data(dataset, new_meta, mirroring_mask)
+    _mirror_videos(dataset, new_meta, vcodec, num_workers)
+    _copy_episodes_metadata(dataset, new_meta)
+
+    logger.info(f"Mirrored dataset saved to: {output_root}")
+    return LeRobotDataset(output_repo_id, root=output_root)
+
+
+def _mirror_data(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    mirroring_mask: dict[str, int],
+) -> None:
+    """Mirror parquet data files."""
+    data_dir = src_dataset.root / DATA_DIR
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+
+    if not parquet_files:
+        raise ValueError(f"No parquet files found in {data_dir}")
+
+    action_names = src_dataset.meta.features.get("action", {}).get("names", [])
+    state_names = src_dataset.meta.features.get("observation.state", {}).get("names", [])
+
+    for src_path in tqdm(parquet_files, desc="Mirroring data files"):
+        df = pd.read_parquet(src_path).reset_index(drop=True)
+        relative_path = src_path.relative_to(src_dataset.root)
+        chunk_dir = relative_path.parts[1]
+        file_name = relative_path.parts[2]
+        chunk_idx = int(chunk_dir.split("-")[1])
+        file_idx = int(file_name.split("-")[1].split(".")[0])
+
+        if "action" in df.columns and action_names:
+            actions = np.stack(df["action"].values)
+            mirrored_actions = np.array([
+                mirror_array(row, action_names, mirroring_mask) for row in actions
+            ])
+            df["action"] = list(mirrored_actions)
+
+        if "observation.state" in df.columns and state_names:
+            states = np.stack(df["observation.state"].values)
+            mirrored_states = np.array([
+                mirror_array(row, state_names, mirroring_mask) for row in states
+            ])
+            df["observation.state"] = list(mirrored_states)
+
+        dst_path = dst_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+        dst_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_parquet(dst_path, index=False)
+
+
+def _mirror_videos(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    vcodec: str,
+    num_workers: int | None = None,
+) -> None:
+    """Mirror video files by flipping horizontally and swapping left/right names."""
+    if not src_dataset.meta.video_keys:
+        return
+
+    video_tasks = []
+    for old_video_key in src_dataset.meta.video_keys:
+        new_video_key = swap_left_right_name(old_video_key)
+        for ep_idx in range(src_dataset.meta.total_episodes):
+            try:
+                src_path = src_dataset.root / src_dataset.meta.get_video_file_path(ep_idx, old_video_key)
+                dst_relative = src_dataset.meta.get_video_file_path(ep_idx, old_video_key)
+                dst_relative_str = str(dst_relative).replace(old_video_key, new_video_key)
+                dst_path = dst_meta.root / dst_relative_str
+                if src_path.exists():
+                    video_tasks.append((src_path, dst_path))
+            except KeyError:
+                continue
+
+    def process_video(task, pbar):
+        src_path, dst_path = task
+        pbar.set_postfix_str(src_path.name)
+        flip_video_frames(src_path, dst_path, src_dataset.meta.fps, vcodec)
+        return src_path
+
+    if num_workers is None:
+        num_workers = os.cpu_count() or 16
+    num_workers = min(len(video_tasks), num_workers)
+    logger.info(f"Processing {len(video_tasks)} videos with {num_workers} workers")
+    with tqdm(total=len(video_tasks), desc="Mirroring videos") as pbar:
+        with ThreadPoolExecutor(max_workers=num_workers) as executor:
+            futures = {executor.submit(process_video, t, pbar): t for t in video_tasks}
+            for future in as_completed(futures):
+                task = futures[future]
+                future.result()
+                pbar.set_postfix_str(f"done: {task[0].name}")
+                pbar.update(1)
+
+
+def _copy_episodes_metadata(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+) -> None:
+    """Copy episodes metadata with swapped video keys."""
+    episodes_dir = src_dataset.root / "meta/episodes"
+    dst_episodes_dir = dst_meta.root / "meta/episodes"
+
+    if episodes_dir.exists():
+        dst_episodes_dir.mkdir(parents=True, exist_ok=True)
+        for src_parquet in episodes_dir.glob("*/*.parquet"):
+            df = pd.read_parquet(src_parquet)
+            columns_to_rename = {}
+            for col in df.columns:
+                if col.startswith("videos/"):
+                    parts = col.split("/")
+                    if len(parts) >= 2:
+                        video_key = parts[1]
+                        new_video_key = swap_left_right_name(video_key)
+                        new_col = col.replace(f"videos/{video_key}/", f"videos/{new_video_key}/")
+                        columns_to_rename[col] = new_col
+            if columns_to_rename:
+                df = df.rename(columns=columns_to_rename)
+            dst_parquet = dst_episodes_dir / src_parquet.relative_to(episodes_dir)
+            dst_parquet.parent.mkdir(parents=True, exist_ok=True)
+            df.to_parquet(dst_parquet, index=False)
+
+    dst_meta.info.update({
+        "total_episodes": src_dataset.meta.total_episodes,
+        "total_frames": src_dataset.meta.total_frames,
+        "total_tasks": src_dataset.meta.total_tasks,
+        "total_videos": src_dataset.meta.total_videos,
+        "total_chunks": src_dataset.meta.total_chunks,
+    })
+    write_info(dst_meta.info, dst_meta.root)
+
+    if src_dataset.meta.stats is not None:
+        mirrored_stats = _mirror_stats(src_dataset.meta.stats)
+        write_stats(mirrored_stats, dst_meta.root)
+
+
+def _mirror_stats(stats: dict) -> dict:
+    """Mirror stats by swapping left/right in feature names."""
+    mirrored = {}
+    for key, value in stats.items():
+        new_key = swap_left_right_name(key)
+        if isinstance(value, dict):
+            mirrored[new_key] = _mirror_stats(value)
+        else:
+            mirrored[new_key] = value
+    return mirrored
+
+
+def main():
+    logging.basicConfig(level=logging.INFO)
+    parser = argparse.ArgumentParser(description="Mirror a bimanual robot dataset")
+    parser.add_argument("--repo_id", type=str, required=True, help="Source dataset repo_id")
+    parser.add_argument("--output_repo_id", type=str, required=True, help="Output dataset repo_id")
+    parser.add_argument("--root", type=str, default=None, help="Source dataset root directory")
+    parser.add_argument("--output_root", type=str, default=None, help="Output dataset root directory")
+    parser.add_argument("--vcodec", type=str, default="libsvtav1", help="Video codec (libsvtav1, h264, hevc)")
+    parser.add_argument("--num_workers", type=int, default=None, help="Number of parallel workers for video processing")
+    parser.add_argument("--push-to-hub", action="store_true", help="Push mirrored dataset to HuggingFace Hub")
+    args = parser.parse_args()
+
+    dataset = mirror_dataset(
+        repo_id=args.repo_id,
+        output_repo_id=args.output_repo_id,
+        root=args.root,
+        output_root=args.output_root,
+        vcodec=args.vcodec,
+        num_workers=args.num_workers,
+    )
+
+    if getattr(args, "push_to_hub", False):
+        logger.info(f"Pushing dataset to HuggingFace Hub: {args.output_repo_id}")
+        dataset.push_to_hub()
+
+
+if __name__ == "__main__":
+    main()
+

src/lerobot/scripts/lerobot_train.py

@@ -175,8 +175,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         from accelerate.utils import DistributedDataParallelKwargs
 
         ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
-        # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
-        # Force the device to be CPU when policy.device is set to CPU.
         force_cpu = cfg.policy.device == "cpu"
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
@@ -211,16 +209,98 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     torch.backends.cuda.matmul.allow_tf32 = True
 
     # Dataset loading synchronization: main process downloads first to avoid race conditions
+    delta_action_stats = None
     if is_main_process:
         logging.info("Creating dataset")
         dataset = make_dataset(cfg)
 
+        # Compute delta action stats BEFORE distributed sync to avoid NCCL timeout
+        if getattr(cfg.policy, "use_delta_actions", False):
+            import numpy as np
+
+            from lerobot.datasets.compute_stats import get_feature_stats
+            from lerobot.processor.delta_action_processor import DeltaActionsProcessorStep, to_delta_actions
+
+            chunk_size = cfg.policy.chunk_size
+            hf = dataset.hf_dataset
+            total_frames = len(hf)
+            sample_upper_bound = total_frames - chunk_size
+            if sample_upper_bound <= 0:
+                raise ValueError(
+                    f"Cannot compute delta action stats: total_frames={total_frames}, chunk_size={chunk_size}"
+                )
+
+            max_samples = min(100_000, sample_upper_bound)
+            indices = np.random.choice(sample_upper_bound, max_samples, replace=False)
+
+            action_names = dataset.meta.features.get("action", {}).get("names")
+            delta_mask_step = DeltaActionsProcessorStep(
+                enabled=True,
+                exclude_joints=getattr(cfg.policy, "delta_exclude_joints", []),
+                action_names=action_names,
+            )
+            delta_mask = delta_mask_step._build_mask(dataset.meta.features["action"]["shape"][0])
+            logging.info(
+                f"use_delta_actions is enabled — computing delta action stats "
+                f"from {max_samples} chunk samples (chunk_size={chunk_size})"
+            )
+
+            all_delta_actions = []
+            episode_indices = np.array(hf["episode_index"])
+            for idx in indices:
+                idx = int(idx)
+                ep_idx = episode_indices[idx]
+                end_idx = min(idx + chunk_size, total_frames)
+                if end_idx > idx and episode_indices[end_idx - 1] != ep_idx:
+                    continue
+
+                chunk_data = hf[idx:end_idx]
+                actions = torch.tensor(np.stack([np.asarray(a) for a in chunk_data["action"]])).float()
+                state = torch.tensor(np.asarray(chunk_data["observation.state"][0])).float()
+
+                delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), delta_mask).squeeze(0)
+                all_delta_actions.append(delta.numpy())
+
+            if not all_delta_actions:
+                raise RuntimeError("Failed to compute delta action stats: no valid chunks found.")
+
+            all_delta = np.concatenate(all_delta_actions, axis=0)
+            delta_stats = get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
+            delta_action_stats = delta_stats
+            dataset.meta.stats["action"] = delta_action_stats
+
+            norm_type = "UNKNOWN"
+            if hasattr(cfg.policy, "normalization_mapping"):
+                from lerobot.configs.types import NormalizationMode
+                action_norm = cfg.policy.normalization_mapping.get("ACTION", None)
+                norm_type = action_norm.value if action_norm else "UNKNOWN"
+
+            excluded_dims = len(delta_mask) - sum(delta_mask)
+            logging.info(
+                f"Delta action stats ({len(all_delta_actions)} chunks, {len(all_delta)} values, norm={norm_type}): "
+                f"delta_dims={sum(delta_mask)}/{len(delta_mask)} (excluded={excluded_dims}), "
+                f"mean={np.abs(delta_stats['mean']).mean():.4f}, std={delta_stats['std'].mean():.4f}, "
+                f"q01={delta_stats['q01'].mean():.4f}, q99={delta_stats['q99'].mean():.4f}"
+            )
+            if norm_type == "QUANTILES":
+                q_range = (delta_stats['q99'] - delta_stats['q01']).mean()
+                logging.info(f"  Quantile range (q99-q01): {q_range:.4f}")
+
     accelerator.wait_for_everyone()
 
     # Now all other processes can safely load the dataset
     if not is_main_process:
         dataset = make_dataset(cfg)
 
+    # Ensure all ranks use the exact same delta action stats.
+    if getattr(cfg.policy, "use_delta_actions", False):
+        if accelerator.num_processes > 1 and torch.distributed.is_initialized():
+            stats_list = [delta_action_stats]
+            torch.distributed.broadcast_object_list(stats_list, src=0)
+            delta_action_stats = stats_list[0]
+        if delta_action_stats is not None:
+            dataset.meta.stats["action"] = delta_action_stats
+
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
     # using the eval.py instead, with gym_dora environment and dora-rs.
@@ -246,10 +326,22 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     # Wait for all processes to finish policy creation before continuing
     accelerator.wait_for_everyone()
 
+    processor_pretrained_path = cfg.policy.pretrained_path
+    if (
+        getattr(cfg.policy, "use_delta_actions", False)
+        and processor_pretrained_path is not None
+        and not cfg.resume
+    ):
+        logging.warning(
+            "use_delta_actions=true with pretrained processors can skip delta transforms if "
+            "the checkpoint processors do not define them. Building processors from current policy config."
+        )
+        processor_pretrained_path = None
+
     # Create processors - only provide dataset_stats if not resuming from saved processors
     processor_kwargs = {}
     postprocessor_kwargs = {}
-    if (cfg.policy.pretrained_path and not cfg.resume) or not cfg.policy.pretrained_path:
+    if (processor_pretrained_path and not cfg.resume) or not processor_pretrained_path:
         # Only provide dataset_stats when not resuming from saved processor state
         processor_kwargs["dataset_stats"] = dataset.meta.stats
 
@@ -257,7 +349,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     if cfg.policy.type == "sarm":
         processor_kwargs["dataset_meta"] = dataset.meta
 
-    if cfg.policy.pretrained_path is not None:
+    if processor_pretrained_path is not None:
         processor_kwargs["preprocessor_overrides"] = {
             "device_processor": {"device": device.type},
             "normalizer_processor": {
@@ -279,7 +371,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
 
     preprocessor, postprocessor = make_pre_post_processors(
         policy_cfg=cfg.policy,
-        pretrained_path=cfg.policy.pretrained_path,
+        pretrained_path=processor_pretrained_path,
         **processor_kwargs,
         **postprocessor_kwargs,
     )
@@ -397,7 +489,36 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     for _ in range(step, cfg.steps):
         start_time = time.perf_counter()
         batch = next(dl_iter)
+
+        # Debug logging for first few steps and periodically
+        if is_main_process and (step < 3 or (cfg.log_freq > 0 and step % (cfg.log_freq * 10) == 0)):
+            action = batch.get("action")
+            state = batch.get("observation.state")
+            if action is not None and state is not None:
+                logging.info(
+                    f"[DEBUG step={step}] PRE-PROCESSOR — "
+                    f"action: shape={tuple(action.shape)}, mean={action.mean():.4f}, std={action.std():.4f}, "
+                    f"min={action.min():.4f}, max={action.max():.4f} | "
+                    f"state: shape={tuple(state.shape)}, mean={state.mean():.4f}"
+                )
+
         batch = preprocessor(batch)
+
+        if is_main_process and (step < 3 or (cfg.log_freq > 0 and step % (cfg.log_freq * 10) == 0)):
+            action = batch.get("action")
+            state = batch.get("observation.state")
+            if action is not None:
+                logging.info(
+                    f"[DEBUG step={step}] POST-PROCESSOR — "
+                    f"action: shape={tuple(action.shape)}, mean={action.mean():.4f}, std={action.std():.4f}, "
+                    f"min={action.min():.4f}, max={action.max():.4f}"
+                )
+                if state is not None:
+                    logging.info(
+                        f"[DEBUG step={step}] POST-PROCESSOR — "
+                        f"state: shape={tuple(state.shape)}, mean={state.mean():.4f}, std={state.std():.4f}"
+                    )
+
         train_tracker.dataloading_s = time.perf_counter() - start_time
 
         train_tracker, output_dict = update_policy(

src/lerobot/scripts/lerobot_train_tokenizer.py

@@ -166,9 +166,9 @@ def apply_normalization(
         if q01 is None or q99 is None:
             raise ValueError("QUANTILES mode requires 'q01' and 'q99' in stats")
         denom = np.maximum(q99 - q01, eps)
-        # No clipping: match training pipeline NormalizerProcessorStep so tokenizer
-        # is fit on the full range of normalized values (including tails outside [-1, 1]).
-        return 2.0 * (data - q01) / denom - 1.0
+        # Clip to quantile range then normalize to [-1, 1]
+        clipped = np.clip(data, q01, q99)
+        return 2.0 * (clipped - q01) / denom - 1.0
 
     if mode == NormalizationMode.QUANTILE10:
         q10 = stats.get("q10")
@@ -176,8 +176,9 @@ def apply_normalization(
         if q10 is None or q90 is None:
             raise ValueError("QUANTILE10 mode requires 'q10' and 'q90' in stats")
         denom = np.maximum(q90 - q10, eps)
-        # No clipping: match training pipeline NormalizerProcessorStep.
-        return 2.0 * (data - q10) / denom - 1.0
+        # Clip to quantile range then normalize to [-1, 1]
+        clipped = np.clip(data, q10, q90)
+        return 2.0 * (clipped - q10) / denom - 1.0
 
     raise ValueError(f"Unsupported normalization mode: {mode}")
 
@@ -305,7 +306,7 @@ def train_fast_tokenizer(
 
     # download the tokenizer source code (not pretrained weights)
     # we'll train a new tokenizer on our own data
-    base_tokenizer = AutoProcessor.from_pretrained("/fsx/jade_choghari/outputs/libero_tokenizer_wavetoken1", trust_remote_code=True)
+    base_tokenizer = AutoProcessor.from_pretrained("physical-intelligence/fast", trust_remote_code=True)
 
     # convert action_chunks array to list of arrays (expected by .fit())
     action_data_list = [action_chunks[i] for i in range(len(action_chunks))]
@@ -319,8 +320,6 @@ def train_fast_tokenizer(
         vocab_size=vocab_size,
         time_horizon=action_chunks.shape[1],  # action_horizon
         action_dim=action_chunks.shape[2],  # encoded dimensions
-        wavelet="dmey",
-        level=1,
     )
     print("✓ Tokenizer training complete!")
 

src/lerobot/teleoperators/so_leader/config_so_leader.py

@@ -28,7 +28,7 @@ class SOLeaderConfig:
     port: str
 
     # Whether to use degrees for angles
-    use_degrees: bool = False
+    use_degrees: bool = True
 
 
 @TeleoperatorConfig.register_subclass("so101_leader")

tests/policies/test_delta_actions.py

@@ -0,0 +1,344 @@
+"""Tests for delta action transforms — full pipeline validation.
+
+Tests the complete flow matching OpenPI:
+  raw actions → DeltaActions → Normalize(delta_stats) → model → Unnormalize → AbsoluteActions
+
+Uses real dataset: lerobot-data-collection/dagger_final_1_21
+"""
+
+import numpy as np
+import pytest
+import torch
+
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.datasets.compute_stats import get_feature_stats
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.processor import TransitionKey, batch_to_transition
+from lerobot.processor.delta_action_processor import (
+    AbsoluteActionsProcessorStep,
+    DeltaActionsProcessorStep,
+    to_absolute_actions,
+    to_delta_actions,
+)
+from lerobot.processor.normalize_processor import NormalizerProcessorStep, UnnormalizerProcessorStep
+from lerobot.utils.constants import ACTION, OBS_STATE
+
+CHUNK_SIZE = 10
+REPO_ID = "lerobot-data-collection/dagger_final_1_21"
+
+
+@pytest.fixture(scope="module")
+def dataset():
+    return LeRobotDataset(REPO_ID, episodes=[0])
+
+
+@pytest.fixture(scope="module")
+def action_dim(dataset):
+    return dataset.meta.features["action"]["shape"][0]
+
+
+def _build_action_chunks(dataset, chunk_size, max_chunks=50):
+    """Build action chunks from hf_dataset, like the training script does."""
+    hf = dataset.hf_dataset
+    total = len(hf)
+    all_ep = torch.tensor([int(hf[i]["episode_index"]) for i in range(total)])
+    chunks, states = [], []
+    for i in range(total - chunk_size + 1):
+        if all_ep[i] != all_ep[i + chunk_size - 1]:
+            continue
+        chunk_actions = torch.stack([hf[i + k]["action"] for k in range(chunk_size)]).float()
+        state = hf[i]["observation.state"].float()
+        chunks.append(chunk_actions)
+        states.append(state)
+        if len(chunks) >= max_chunks:
+            break
+    assert len(chunks) > 0, f"No valid chunks found. total={total}, ep_indices={all_ep.tolist()}"
+    return torch.stack(chunks), torch.stack(states)
+
+
+def _compute_delta_chunk_stats(action_chunks, states, mask):
+    all_deltas = []
+    for actions, state in zip(action_chunks, states):
+        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
+        all_deltas.append(delta.numpy())
+    all_delta = np.concatenate(all_deltas, axis=0)
+    return get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
+
+
+# --- Basic roundtrip tests ---
+
+def test_roundtrip_3d(action_dim):
+    actions = torch.randn(4, CHUNK_SIZE, action_dim)
+    state = torch.randn(4, action_dim)
+    mask = [True] * action_dim
+    recovered = to_absolute_actions(to_delta_actions(actions, state, mask), state, mask)
+    torch.testing.assert_close(recovered, actions)
+
+
+def test_roundtrip_2d(action_dim):
+    actions = torch.randn(4, action_dim)
+    state = torch.randn(4, action_dim)
+    mask = [True] * action_dim
+    recovered = to_absolute_actions(to_delta_actions(actions, state, mask), state, mask)
+    torch.testing.assert_close(recovered, actions)
+
+
+def test_no_mutation(action_dim):
+    actions = torch.randn(2, CHUNK_SIZE, action_dim)
+    original = actions.clone()
+    state = torch.randn(2, action_dim)
+    to_delta_actions(actions, state, [True] * action_dim)
+    torch.testing.assert_close(actions, original)
+
+
+def test_exclude_joints_supports_partial_name_matching():
+    names = [
+        "right_joint_1.pos",
+        "right_gripper.pos",
+        "left_joint_1.pos",
+        "left_gripper.pos",
+    ]
+    step = DeltaActionsProcessorStep(enabled=True, exclude_joints=["gripper"], action_names=names)
+    assert step._build_mask(len(names)) == [True, False, True, False]
+
+
+# --- Chunk-level delta stats test ---
+
+def test_chunk_stats_have_larger_std_than_frame_stats(dataset, action_dim):
+    """Chunk-level delta stats should have larger std than per-frame delta stats."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+    mask = [True] * action_dim
+
+    chunk_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
+
+    # Per-frame stats
+    hf = dataset.hf_dataset
+    n = min(500, len(hf))
+    frame_actions = torch.stack([hf[i]["action"] for i in range(n)]).float()
+    frame_states = torch.stack([hf[i]["observation.state"] for i in range(n)]).float()
+    frame_deltas = to_delta_actions(frame_actions, frame_states, mask).numpy()
+    frame_stats = get_feature_stats(frame_deltas, axis=0, keepdims=frame_deltas.ndim == 1)
+
+    assert chunk_stats["std"].mean() >= frame_stats["std"].mean(), (
+        f"Chunk std ({chunk_stats['std'].mean():.4f}) should be >= "
+        f"frame std ({frame_stats['std'].mean():.4f})"
+    )
+
+
+# --- Full pipeline roundtrip: delta → normalize → unnormalize → absolute ---
+
+def test_full_pipeline_roundtrip(dataset, action_dim):
+    """Test the complete OpenPI pipeline: delta → normalize → unnormalize → absolute."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+    mask = [True] * action_dim
+
+    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
+    stats = {ACTION: {k: v for k, v in delta_stats.items()}}
+
+    features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))}
+    norm_map = {FeatureType.ACTION: NormalizationMode.MEAN_STD}
+
+    delta_step = DeltaActionsProcessorStep(enabled=True)
+    normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
+    unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
+    absolute_step = AbsoluteActionsProcessorStep(enabled=True, delta_step=delta_step)
+
+    original_actions = action_chunks[0].unsqueeze(0)
+    state = states[0].unsqueeze(0)
+
+    batch = {ACTION: original_actions, OBS_STATE: state}
+    transition = batch_to_transition(batch)
+
+    # Forward: delta → normalize
+    t1 = delta_step(transition)
+    t2 = normalizer(t1)
+
+    normalized_action = t2[TransitionKey.ACTION]
+    assert normalized_action.abs().mean() < 10, (
+        f"Normalized actions should be in reasonable range, got mean abs {normalized_action.abs().mean():.2f}"
+    )
+
+    # Reverse: unnormalize → absolute
+    t3 = unnormalizer(t2)
+    t4 = absolute_step(t3)
+
+    recovered_actions = t4[TransitionKey.ACTION]
+    torch.testing.assert_close(recovered_actions, original_actions, atol=1e-4, rtol=1e-4)
+
+
+def test_normalized_delta_values_are_reasonable(dataset, action_dim):
+    """With correct chunk stats, normalized delta actions should be in a reasonable range."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+    mask = [True] * action_dim
+
+    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
+    mean = torch.tensor(delta_stats["mean"]).float()
+    std = torch.tensor(delta_stats["std"]).float()
+
+    all_normalized = []
+    for actions, state in zip(action_chunks, states):
+        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
+        normalized = (delta - mean) / (std + 1e-6)
+        all_normalized.append(normalized)
+
+    all_normalized = torch.cat(all_normalized, dim=0)
+
+    pct_in_range = (all_normalized.abs() < 5).float().mean()
+    assert pct_in_range > 0.9, (
+        f"Only {pct_in_range*100:.1f}% of normalized values in [-5, 5], expected >90%"
+    )
+
+    assert all_normalized.mean().abs() < 1.0, (
+        f"Mean of normalized deltas is {all_normalized.mean():.2f}, expected near 0"
+    )
+
+
+def test_processor_step_roundtrip(dataset, action_dim):
+    """DeltaActionsProcessorStep applies delta; to_absolute_actions recovers original."""
+    hf = dataset.hf_dataset
+    batch = {
+        ACTION: torch.stack([hf[i]["action"] for i in range(4)]),
+        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(4)]),
+    }
+    original_actions = batch[ACTION].clone()
+    transition = batch_to_transition(batch)
+
+    step = DeltaActionsProcessorStep(enabled=True)
+    delta_transition = step(transition)
+    assert not torch.allclose(delta_transition[TransitionKey.ACTION], original_actions)
+
+    state = transition[TransitionKey.OBSERVATION][OBS_STATE]
+    mask = [True] * action_dim
+    recovered = to_absolute_actions(delta_transition[TransitionKey.ACTION], state, mask)
+    torch.testing.assert_close(recovered, original_actions)
+
+
+def test_processor_step_disabled_is_noop(dataset, action_dim):
+    """enabled=False should be a no-op."""
+    hf = dataset.hf_dataset
+    batch = {
+        ACTION: torch.stack([hf[i]["action"] for i in range(2)]),
+        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(2)]),
+    }
+    original = batch[ACTION].clone()
+    transition = batch_to_transition(batch)
+    result = DeltaActionsProcessorStep(enabled=False)(transition)
+    torch.testing.assert_close(result[TransitionKey.ACTION], original)
+
+
+# --- Training batch shape validation ---
+
+def test_delta_with_action_chunks(dataset, action_dim):
+    """Verify delta works correctly with (B, chunk_size, action_dim) shaped actions."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+
+    # Simulate a training batch: actions=(B, chunk_size, action_dim), state=(B, state_dim)
+    batch_actions = action_chunks[:4]  # (4, chunk_size, action_dim)
+    batch_states = states[:4]  # (4, state_dim)
+
+    mask = [True] * action_dim
+    delta = to_delta_actions(batch_actions, batch_states, mask)
+
+    # First action in each chunk should be close to zero (action[t] - state[t] ≈ small)
+    first_deltas = delta[:, 0, :]  # (B, action_dim)
+    assert first_deltas.abs().mean() < delta.abs().mean(), (
+        f"First action in chunk should have smaller delta than average. "
+        f"First: {first_deltas.abs().mean():.4f}, Average: {delta.abs().mean():.4f}"
+    )
+
+    # Later actions should have larger deltas
+    last_deltas = delta[:, -1, :]  # (B, action_dim)
+    assert last_deltas.abs().mean() >= first_deltas.abs().mean(), (
+        f"Last action in chunk should have >= delta than first. "
+        f"Last: {last_deltas.abs().mean():.4f}, First: {first_deltas.abs().mean():.4f}"
+    )
+
+    # Roundtrip
+    recovered = to_absolute_actions(delta, batch_states, mask)
+    torch.testing.assert_close(recovered, batch_actions)
+
+
+def test_delta_stats_match_actual_data_distribution(dataset, action_dim):
+    """Verify computed stats match the actual delta distribution."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+    mask = [True] * action_dim
+
+    # Compute stats like the training script does
+    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
+
+    # Also compute directly
+    all_deltas = []
+    for actions, state in zip(action_chunks, states):
+        delta = to_delta_actions(actions.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
+        all_deltas.append(delta)
+    all_deltas_tensor = torch.cat(all_deltas, dim=0)
+
+    # Compare mean
+    actual_mean = all_deltas_tensor.mean(dim=0).numpy()
+    np.testing.assert_allclose(delta_stats["mean"], actual_mean, atol=0.01)
+
+    # Compare std
+    actual_std = all_deltas_tensor.std(dim=0).numpy()
+    np.testing.assert_allclose(delta_stats["std"], actual_std, atol=0.1)
+
+    # Verify q01 < mean < q99
+    assert (delta_stats["q01"] < delta_stats["mean"]).all(), "q01 should be < mean"
+    assert (delta_stats["mean"] < delta_stats["q99"]).all(), "mean should be < q99"
+
+
+def test_quantile_normalization_roundtrip(dataset, action_dim):
+    """Full roundtrip with QUANTILES normalization (what OpenPI uses for pi05)."""
+    action_chunks, states = _build_action_chunks(dataset, CHUNK_SIZE)
+    mask = [True] * action_dim
+
+    delta_stats = _compute_delta_chunk_stats(action_chunks, states, mask)
+    stats = {ACTION: {k: v for k, v in delta_stats.items()}}
+
+    features = {ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))}
+    norm_map = {FeatureType.ACTION: NormalizationMode.QUANTILES}
+
+    delta_step = DeltaActionsProcessorStep(enabled=True)
+    normalizer = NormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
+    unnormalizer = UnnormalizerProcessorStep(features=features, norm_map=norm_map, stats=stats)
+    absolute_step = AbsoluteActionsProcessorStep(enabled=True, delta_step=delta_step)
+
+    original_actions = action_chunks[0].unsqueeze(0)
+    state = states[0].unsqueeze(0)
+
+    batch = {ACTION: original_actions, OBS_STATE: state}
+    transition = batch_to_transition(batch)
+
+    # Forward: delta → quantile normalize
+    t1 = delta_step(transition)
+    t2 = normalizer(t1)
+
+    normalized = t2[TransitionKey.ACTION]
+    # Most values should be in [-1, 1] with quantile normalization
+    pct_in_range = (normalized.abs() < 2).float().mean()
+    assert pct_in_range > 0.5, (
+        f"Only {pct_in_range*100:.1f}% in [-2, 2] after quantile norm, expected >50%"
+    )
+
+    # Reverse: unnormalize → absolute
+    t3 = unnormalizer(t2)
+    t4 = absolute_step(t3)
+
+    recovered = t4[TransitionKey.ACTION]
+    torch.testing.assert_close(recovered, original_actions, atol=1e-3, rtol=1e-3)
+
+
+def test_state_not_modified_by_delta(dataset, action_dim):
+    """State should never be modified by the delta processor."""
+    hf = dataset.hf_dataset
+    batch = {
+        ACTION: torch.stack([hf[i]["action"] for i in range(4)]),
+        OBS_STATE: torch.stack([hf[i]["observation.state"] for i in range(4)]),
+    }
+    original_state = batch[OBS_STATE].clone()
+    transition = batch_to_transition(batch)
+
+    step = DeltaActionsProcessorStep(enabled=True)
+    result = step(transition)
+
+    result_state = result[TransitionKey.OBSERVATION][OBS_STATE]
+    torch.testing.assert_close(result_state, original_state)

tests/scripts/test_edit_dataset_parsing.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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.
+
+import draccus
+import pytest
+
+from lerobot.scripts.lerobot_edit_dataset import (
+    ConvertImageToVideoConfig,
+    DeleteEpisodesConfig,
+    EditDatasetConfig,
+    MergeConfig,
+    ModifyTasksConfig,
+    OperationConfig,
+    RemoveFeatureConfig,
+    SplitConfig,
+)
+
+
+def parse_cfg(cli_args: list[str]) -> EditDatasetConfig:
+    """Helper to parse CLI args into an EditDatasetConfig via draccus."""
+    return draccus.parse(EditDatasetConfig, args=cli_args)
+
+
+class TestOperationTypeParsing:
+    """Test that --operation.type correctly selects the right config subclass."""
+
+    @pytest.mark.parametrize(
+        "type_name, expected_cls",
+        [
+            ("delete_episodes", DeleteEpisodesConfig),
+            ("split", SplitConfig),
+            ("merge", MergeConfig),
+            ("remove_feature", RemoveFeatureConfig),
+            ("modify_tasks", ModifyTasksConfig),
+            ("convert_image_to_video", ConvertImageToVideoConfig),
+        ],
+    )
+    def test_operation_type_resolves_correct_class(self, type_name, expected_cls):
+        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
+        assert isinstance(cfg.operation, expected_cls), (
+            f"Expected {expected_cls.__name__}, got {type(cfg.operation).__name__}"
+        )
+
+    @pytest.mark.parametrize(
+        "type_name, expected_cls",
+        [
+            ("delete_episodes", DeleteEpisodesConfig),
+            ("split", SplitConfig),
+            ("merge", MergeConfig),
+            ("remove_feature", RemoveFeatureConfig),
+            ("modify_tasks", ModifyTasksConfig),
+            ("convert_image_to_video", ConvertImageToVideoConfig),
+        ],
+    )
+    def test_get_choice_name_roundtrips(self, type_name, expected_cls):
+        cfg = parse_cfg(["--repo_id", "test/repo", "--operation.type", type_name])
+        resolved_name = OperationConfig.get_choice_name(type(cfg.operation))
+        assert resolved_name == type_name