add various experiments for wavelet

* feat(cameras): make backend configurable to the CLI

* chore(cameras): address feedback

* feat(Enum error messages): adding better instanciation error messages for Enum classes

* chore(Enum error messages): propagating Enum error messages to all camera classes

* chore(comments): removing superfluous comments

* chore(format): applying ruff checks

---------

Co-authored-by: CarolinePascal <caroline8.pascal@gmail.com>

.github/workflows/full_tests.yml

@@ -101,9 +101,11 @@ jobs:
     runs-on:
       group: aws-general-8-plus
     if: |
-      (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
-      github.event_name == 'push' ||
-      github.event_name == 'workflow_dispatch'
+      github.repository == 'huggingface/lerobot' && (
+        (github.event_name == 'pull_request_review' && github.event.review.state == 'approved' && github.event.pull_request.head.repo.fork == false) ||
+        github.event_name == 'push' ||
+        github.event_name == 'workflow_dispatch'
+      )
     outputs:
       image_tag: ${{ steps.set_tag.outputs.image_tag }}
     env:

.github/workflows/unbound_deps_tests.yml

@@ -91,6 +91,7 @@ jobs:
     name: Build and Push Docker
     runs-on:
       group: aws-general-8-plus
+    if: github.repository == 'huggingface/lerobot'
     outputs:
       image_tag: ${{ env.DOCKER_IMAGE_NAME }}
     env:

benchmarks/tokens/README.md

@@ -0,0 +1,134 @@
+# 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

@@ -0,0 +1,442 @@
+#!/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,
+    )

docs/source/_toctree.yml

@@ -7,8 +7,6 @@
 - sections:
   - local: il_robots
     title: Imitation Learning for Robots
-  - local: cameras
-    title: Cameras
   - local: bring_your_own_policies
     title: Bring Your Own Policies
   - local: integrate_hardware
@@ -29,6 +27,8 @@
     title: Porting Large Datasets
   - local: using_dataset_tools
     title: Using the Dataset Tools
+  - local: dataset_subtask
+    title: Using Subtasks in the Dataset
   title: "Datasets"
 - sections:
   - local: act
@@ -101,11 +101,17 @@
     title: Earth Rover Mini
   - local: omx
     title: OMX
+  - local: openarm
+    title: OpenArm
   title: "Robots"
 - sections:
   - local: phone_teleop
     title: Phone
   title: "Teleoperators"
+- sections:
+  - local: cameras
+    title: Cameras
+  title: "Sensors"
 - sections:
   - local: torch_accelerators
     title: PyTorch accelerators

docs/source/cameras.mdx

@@ -1,12 +1,22 @@
 # Cameras
 
-LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
+LeRobot offers multiple options for video capture:
 
-### Finding your camera
+| Class             | Supported Cameras                   |
+| ----------------- | ----------------------------------- |
+| `OpenCVCamera`    | Phone, built-in laptop, USB webcams |
+| `ZMQCamera`       | Network-connected cameras           |
+| `RealSenseCamera` | Intel RealSense (with depth)        |
+| `Reachy2Camera`   | Reachy 2 robot cameras              |
 
-To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system.
+> [!TIP]
+> For `OpenCVCamera` compatibility details, see the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html).
 
-To find the camera indices of the cameras plugged into your system, run the following script:
+### Find your camera
+
+Every camera requires a unique identifier to be instantiated, allowing you to distinguish between multiple connected devices.
+
+`OpenCVCamera` and `RealSenseCamera` support auto-discovery. Run the command below to list available devices and their identifiers. Note that these identifiers may change after rebooting your computer or re-plugging the camera, depending on your operating system.
 
 ```bash
 lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
@@ -14,7 +24,7 @@ lerobot-find-cameras opencv # or realsense for Intel Realsense cameras
 
 The output will look something like this if you have two cameras connected:
 
-```
+```bash
 --- Detected Cameras ---
 Camera #0:
   Name: OpenCV Camera @ 0
@@ -33,13 +43,37 @@ Camera #0:
 > [!WARNING]
 > When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable.
 
-## Use Cameras
+`ZMQCamera` and `Reachy2Camera` do not support auto-discovery. They must be configured manually by providing their network address and port or robot SDK settings.
 
-Below are two examples, demonstrating how to work with the API.
+## Use cameras
 
-- **Asynchronous frame capture** using an OpenCV-based camera
+### Frame access modes
+
+All camera classes implement three access modes for capturing frames:
+
+| Method                    | Behavior                                                                                                                                                   | Blocks?        | Best For                                 |
+| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | ---------------------------------------- |
+| `read()`                  | Waits for the camera hardware to return a frame. May block for a long time depending on the camera and SDK.                                                | Yes            | Simple scripts, sequential capture       |
+| `async_read(timeout_ms)`  | Returns the latest unconsumed frame from background thread. Blocks only if buffer is empty, up to `timeout_ms`. Raises `TimeoutError` if no frame arrives. | With a timeout | Control loops synchronized to camera FPS |
+| `read_latest(max_age_ms)` | Peeks at the most recent frame in buffer (may be stale). Raises `TimeoutError` if frame is older than `max_age_ms`.                                        | No             | UI visualization, logging, monitoring    |
+
+### Usage examples
+
+The following examples show how to use the camera API to configure and capture frames from different camera types.
+
+- **Blocking and non-blocking frame capture** using an OpenCV-based camera
 - **Color and depth capture** using an Intel RealSense camera
 
+> [!WARNING]
+> Failing to cleanly disconnect cameras can cause resource leaks. Use the context manager protocol to ensure automatic cleanup:
+>
+> ```python
+> with OpenCVCamera(config) as camera:
+>     ...
+> ```
+>
+> You can also call `connect()` and `disconnect()` manually, but always use a `finally` block for the latter.
+
 <hfoptions id="shell_restart">
 <hfoption id="Open CV Camera">
 
@@ -60,16 +94,30 @@ config = OpenCVCameraConfig(
 )
 
 # Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default).
-camera = OpenCVCamera(config)
-camera.connect()
+with OpenCVCamera(config) as camera:
+
+    # Read a frame synchronously — blocks until hardware delivers a new frame
+    frame = camera.read()
+    print(f"read() call returned frame with shape:", frame.shape)
+
+    # Read a frame asynchronously with a timeout — returns the latest unconsumed frame or waits up to timeout_ms for a new one
+    try:
+        for i in range(10):
+            frame = camera.async_read(timeout_ms=200)
+            print(f"async_read call returned frame {i} with shape:", frame.shape)
+    except TimeoutError as e:
+        print(f"No frame received within timeout: {e}")
+
+    # Instantly return a frame - returns the most recent frame captured by the camera
+    try:
+        initial_frame = camera.read_latest(max_age_ms=1000)
+        for i in range(10):
+            frame = camera.read_latest(max_age_ms=1000)
+            print(f"read_latest call returned frame {i} with shape:", frame.shape)
+            print(f"Was a new frame received by the camera? {not (initial_frame == frame).any()}")
+    except TimeoutError as e:
+        print(f"Frame too old: {e}")
 
-# Read frames asynchronously in a loop via `async_read(timeout_ms)`
-try:
-    for i in range(10):
-        frame = camera.async_read(timeout_ms=200)
-        print(f"Async frame {i} shape:", frame.shape)
-finally:
-    camera.disconnect()
 ```
 <!-- prettier-ignore-end -->
 
@@ -111,10 +159,10 @@ finally:
 </hfoption>
 </hfoptions>
 
-## Use your phone
+## Use your phone's camera
 
 <hfoptions id="use phone">
-<hfoption id="Mac">
+<hfoption id="iPhone & macOS">
 
 To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
 
@@ -124,83 +172,49 @@ To use your iPhone as a camera on macOS, enable the Continuity Camera feature:
 
 For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac).
 
-Your iPhone should be detected automatically when running the camera setup script in the next section.
-
 </hfoption>
-<hfoption id="Linux">
+<hfoption id="OBS virtual camera">
 
-If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera
+If you want to use your phone as a camera using OBS, follow these steps to set up a virtual camera.
 
-1. _Install `v4l2loopback-dkms` and `v4l-utils`_. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using:
+1. _(Linux only) Install `v4l2loopback-dkms` and `v4l-utils`_. These packages create virtual camera devices and verify their settings. Install with:
 
-<!-- prettier-ignore-start -->
-```python
+```bash
 sudo apt install v4l2loopback-dkms v4l-utils
 ```
-<!-- prettier-ignore-end -->
 
-2. _Install [DroidCam](https://droidcam.app) on your phone_. This app is available for both iOS and Android.
-3. _Install [OBS Studio](https://obsproject.com)_. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org):
+2. _Install the [DroidCam app](https://droidcam.app) on your phone_. This app is available for both iOS and Android.
+3. _Download and install [OBS Studio](https://obsproject.com)_.
+4. _Download and install the [DroidCam OBS plugin](https://droidcam.app/obs)_.
+5. _Start OBS Studio_.
 
-<!-- prettier-ignore-start -->
-```python
-flatpak install flathub com.obsproject.Studio
-```
-<!-- prettier-ignore-end -->
-
-4. _Install the DroidCam OBS plugin_. This plugin integrates DroidCam with OBS Studio. Install it with:
-
-<!-- prettier-ignore-start -->
-```python
-flatpak install flathub com.obsproject.Studio.Plugin.DroidCam
-```
-<!-- prettier-ignore-end -->
-
-5. _Start OBS Studio_. Launch with:
-
-<!-- prettier-ignore-start -->
-```python
-flatpak run com.obsproject.Studio
-```
-<!-- prettier-ignore-end -->
-
-6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`.
-7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in.
+6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480` to avoid the watermarks.
+7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video` or `OBS > Preferences... > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it.
 8. _Start virtual camera_. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide).
-9. _Verify the virtual camera setup_. Use `v4l2-ctl` to list the devices:
+9. _Verify the virtual camera setup and resolution_.
+   - **Linux**: Use `v4l2-ctl` to list devices and check resolution:
+     ```bash
+     v4l2-ctl --list-devices  # find VirtualCam and note its /dev/videoX path
+     v4l2-ctl -d /dev/videoX --get-fmt-video  # replace with your VirtualCam path
+     ```
+     You should see `VirtualCam` listed and resolution `640x480`.
+   - **macOS**: Open Photo Booth or FaceTime and select "OBS Virtual Camera" as the input.
+   - **Windows**: The native Camera app doesn't support virtual cameras. Use a video conferencing app (Zoom, Teams) or run `lerobot-find-cameras opencv` directly to verify.
 
-<!-- prettier-ignore-start -->
-```python
-v4l2-ctl --list-devices
-```
-<!-- prettier-ignore-end -->
+<details>
+<summary><strong>Troubleshooting</strong></summary>
 
-You should see an entry like:
+> The virtual camera resolution is incorrect.
 
-```
-VirtualCam (platform:v4l2loopback-000):
-/dev/video1
-```
+Delete the virtual camera source and recreate it. The resolution cannot be changed after creation.
 
-10. _Check the camera resolution_. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`.
+> Error reading frame in background thread for OpenCVCamera(X): OpenCVCamera(X) frame width=640 or height=480 do not match configured width=1920 or height=1080.
 
-<!-- prettier-ignore-start -->
-```python
-v4l2-ctl -d /dev/video1 --get-fmt-video
-```
-<!-- prettier-ignore-end -->
+This error is caused by OBS Virtual Camera advertising a `1920x1080` resolution despite rescaling. The only fix for now is to comment out the width and height check in `_postprocess_image()`.
 
-You should see an entry like:
-
-```
->>> Format Video Capture:
->>>	Width/Height      : 640/480
->>>	Pixel Format      : 'YUYV' (YUYV 4:2:2)
-```
-
-Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed.
-
-If everything is set up correctly, you can proceed with the rest of the tutorial.
+</details>
 
 </hfoption>
 </hfoptions>
+
+If everything is set up correctly, your phone will appear as a standard OpenCV camera and can be used with `OpenCVCamera`.

docs/source/dataset_subtask.mdx

@@ -0,0 +1,278 @@
+# Using Subtasks in LeRobot Datasets
+
+Subtask support in robotics datasets has proven effective in improving robot reasoning and understanding. Subtasks are particularly useful for:
+
+- **Hierarchical policies**: Building policies that include subtask predictions to visualize robot reasoning in real time
+- **Reward modeling**: Helping reward models understand task progression (e.g., SARM-style stage-aware reward models)
+- **Task decomposition**: Breaking down complex manipulation tasks into atomic, interpretable steps
+
+LeRobotDataset now supports subtasks as part of its dataset structure, alongside tasks.
+
+## What are Subtasks?
+
+While a **task** describes the overall goal (e.g., "Pick up the apple and place it in the basket"), **subtasks** break down the execution into finer-grained steps:
+
+1. "Approach the apple"
+2. "Grasp the apple"
+3. "Lift the apple"
+4. "Move to basket"
+5. "Release the apple"
+
+Each frame in the dataset can be annotated with its corresponding subtask, enabling models to learn and predict these intermediate stages.
+
+<img
+  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/subtask-asset.png"
+  alt="An overview of subtask annotation showing how frames are labeled with intermediate subtask stages"
+  width="80%"
+/>
+
+<p>
+  <em>Figure: Overview of subtask annotation.</em>
+</p>
+
+**Reference:** _Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing_, Original Article, Published: 19 April 2022.
+
+## Dataset Structure
+
+Subtask information is stored in the dataset metadata:
+
+```
+my-dataset/
+├── data/
+│   └── ...
+├── meta/
+│   ├── info.json
+│   ├── stats.json
+│   ├── tasks.parquet
+│   ├── subtasks.parquet      # Subtask index → subtask string mapping
+│   └── episodes/
+│       └── ...
+└── videos/
+    └── ...
+```
+
+### Subtasks Parquet File
+
+The `meta/subtasks.parquet` file maps subtask indices to their natural language descriptions:
+
+| subtask_index | subtask (index column) |
+| ------------- | ---------------------- |
+| 0             | "Approach the apple"   |
+| 1             | "Grasp the apple"      |
+| 2             | "Lift the apple"       |
+| ...           | ...                    |
+
+### Frame-Level Annotations
+
+Each frame in the dataset can include a `subtask_index` field that references the subtasks parquet file:
+
+```python
+# Example frame data in the parquet file
+{
+    "index": 42,
+    "timestamp": 1.4,
+    "episode_index": 0,
+    "task_index": 0,
+    "subtask_index": 2,  # References "Lift the apple"
+    "observation.state": [...],
+    "action": [...],
+}
+```
+
+## Annotating Datasets with Subtasks
+
+We provide a HuggingFace Space for easily annotating any LeRobotDataset with subtasks:
+
+**[https://huggingface.co/spaces/lerobot/annotate](https://huggingface.co/spaces/lerobot/annotate)**
+
+After completing your annotation:
+
+1. Click "Push to Hub" to upload your annotated dataset
+2. You can also run the annotation space locally by following the instructions at [github.com/huggingface/lerobot-annotate](https://github.com/huggingface/lerobot-annotate)
+
+## Loading Datasets with Subtasks
+
+When you load a dataset with subtask annotations, the subtask information is automatically available:
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Load a dataset with subtask annotations
+dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
+
+# Access a sample
+sample = dataset[100]
+
+# The sample includes both task and subtask information
+print(sample["task"])        # "Collect the fruit"
+print(sample["subtask"])     # "Grasp the apple"
+print(sample["task_index"])  # tensor(0)
+print(sample["subtask_index"])  # tensor(2)
+```
+
+### Checking for Subtask Support
+
+You can check if a dataset has subtask annotations:
+
+```python
+# Check if subtasks are available
+has_subtasks = (
+    "subtask_index" in dataset.features
+    and dataset.meta.subtasks is not None
+)
+
+if has_subtasks:
+    print(f"Dataset has {len(dataset.meta.subtasks)} unique subtasks")
+    print("Subtasks:", list(dataset.meta.subtasks.index))
+```
+
+## Using Subtasks for Training
+
+### With the Tokenizer Processor
+
+The `TokenizerProcessor` automatically handles subtask tokenization for Vision-Language Action (VLA) models:
+
+```python
+from lerobot.processor.tokenizer_processor import TokenizerProcessor
+from lerobot.processor.pipeline import ProcessorPipeline
+
+# Create a tokenizer processor
+tokenizer_processor = TokenizerProcessor(
+    tokenizer_name_or_path="google/paligemma-3b-pt-224",
+    padding="max_length",
+    max_length=64,
+)
+
+# The processor will automatically tokenize subtasks if present in the batch
+# and add them to the observation under:
+# - "observation.subtask.tokens"
+# - "observation.subtask.attention_mask"
+```
+
+When subtasks are available in the batch, the tokenizer processor adds:
+
+- `observation.subtask.tokens`: Tokenized subtask text
+- `observation.subtask.attention_mask`: Attention mask for the subtask tokens
+
+### DataLoader with Subtasks
+
+```python
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
+
+dataloader = torch.utils.data.DataLoader(
+    dataset,
+    batch_size=16,
+    shuffle=True,
+)
+
+for batch in dataloader:
+    # Access subtask information in the batch
+    subtasks = batch["subtask"]  # List of subtask strings
+    subtask_indices = batch["subtask_index"]  # Tensor of subtask indices
+
+    # Use for training hierarchical policies or reward models
+    print(f"Batch subtasks: {set(subtasks)}")
+```
+
+## Example Datasets with Subtask Annotations
+
+Try loading a dataset with subtask annotations:
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Example dataset with subtask annotations
+dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated")
+
+# Explore the subtasks
+print("Available subtasks:")
+for subtask_name in dataset.meta.subtasks.index:
+    print(f"  - {subtask_name}")
+
+# Get subtask distribution
+subtask_counts = {}
+for i in range(len(dataset)):
+    sample = dataset[i]
+    subtask = sample["subtask"]
+    subtask_counts[subtask] = subtask_counts.get(subtask, 0) + 1
+
+print("\nSubtask distribution:")
+for subtask, count in sorted(subtask_counts.items(), key=lambda x: -x[1]):
+    print(f"  {subtask}: {count} frames")
+```
+
+## Use Cases
+
+### 1. Hierarchical Policy Training
+
+Train policies that predict both actions and current subtask:
+
+```python
+class HierarchicalPolicy(nn.Module):
+    def __init__(self, num_subtasks):
+        super().__init__()
+        self.action_head = nn.Linear(hidden_dim, action_dim)
+        self.subtask_head = nn.Linear(hidden_dim, num_subtasks)
+
+    def forward(self, observations):
+        features = self.encoder(observations)
+        actions = self.action_head(features)
+        subtask_logits = self.subtask_head(features)
+        return actions, subtask_logits
+```
+
+### 2. Stage-Aware Reward Modeling (SARM)
+
+Build reward models that understand task progression:
+
+```python
+# SARM predicts:
+# - Stage: Which subtask is being executed (discrete)
+# - Progress: How far along the subtask (continuous 0-1)
+
+class SARMRewardModel(nn.Module):
+    def forward(self, observations):
+        features = self.encoder(observations)
+        stage_logits = self.stage_classifier(features)
+        progress = self.progress_regressor(features)
+        return stage_logits, progress
+```
+
+### 3. Progress Visualization
+
+Monitor robot execution by tracking subtask progression:
+
+```python
+def visualize_execution(model, observations):
+    for t, obs in enumerate(observations):
+        action, subtask_logits = model(obs)
+        predicted_subtask = subtask_names[subtask_logits.argmax()]
+        print(f"t={t}: Executing '{predicted_subtask}'")
+```
+
+## API Reference
+
+### LeRobotDataset Properties
+
+| Property                    | Type                   | Description                                |
+| --------------------------- | ---------------------- | ------------------------------------------ |
+| `meta.subtasks`             | `pd.DataFrame \| None` | DataFrame mapping subtask names to indices |
+| `features["subtask_index"]` | `dict`                 | Feature spec for subtask_index if present  |
+
+### Sample Keys
+
+When subtasks are available, each sample includes:
+
+| Key             | Type           | Description                          |
+| --------------- | -------------- | ------------------------------------ |
+| `subtask_index` | `torch.Tensor` | Integer index of the current subtask |
+| `subtask`       | `str`          | Natural language subtask description |
+
+## Related Resources
+
+- [SARM Paper](https://arxiv.org/pdf/2509.25358) - Stage-Aware Reward Modeling for Long Horizon Robot Manipulation
+- [LeRobot Annotate Space](https://huggingface.co/spaces/lerobot/annotate) - Interactive annotation tool
+- [LeRobotDataset v3.0](./lerobot-dataset-v3) - Dataset format documentation

docs/source/installation.mdx

@@ -1,13 +1,15 @@
 # Installation
 
-## Install [`miniforge`](https://conda-forge.org/download/)
+This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.10 and ffmpeg installed with the `libsvtav1` encoder, then skip ahead to [Install LeRobot](#step-3-install-lerobot-).
+
+## Step 1: Install [`miniforge`](https://conda-forge.org/download/)
 
 ```bash
 wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
 bash Miniforge3-$(uname)-$(uname -m).sh
 ```
 
-## Environment Setup
+## Step 2: Environment Setup
 
 Create a virtual environment with Python 3.10, using conda:
 
@@ -38,7 +40,7 @@ conda install ffmpeg -c conda-forge
 >
 > - _[On Linux only]_ If you want to bring your own ffmpeg: Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
 
-## Install LeRobot 🤗
+## Step 3: Install LeRobot 🤗
 
 ### From Source
 

docs/source/openarm.mdx

@@ -0,0 +1,276 @@
+# OpenArm
+
+[OpenArm](https://openarm.dev) is an open-source 7DOF humanoid arm designed for physical AI research and deployment.
+
+To get your OpenArm, assembled or DIY, and join the global community, browse verified and certified manufacturers worldwide at [openarm.dev](https://openarm.dev).
+
+## What's Unique?
+
+- **Human-Scale Design**: OpenArm is designed with human-like proportions, scaled for a person around 160-165cm tall. This provides an optimal balance between practical reach and manageable inertia for safe, responsive operation.
+
+- **Safety-First Architecture**: Built with QDD backdrivable motors and high compliance, OpenArm prioritizes safe human-robot interaction while maintaining practical payload capabilities (6.0kg peak / 4.1kg nominal) for real-world tasks.
+
+- **Built for Durability**: Critical structural components use aluminum and stainless steel construction, ensuring robust performance for repetitive data collection and continuous research use.
+
+- **Fully Accessible & Buildable**: Every component, from CNC parts and 3D-printed casings to electrical wiring is designed to be purchasable and buildable by individual researchers and labs, with complete fabrication data provided.
+
+- **Practical & Affordable**: At $6,500 USD for a complete bimanual system, OpenArm delivers research-grade capabilities at a fraction of traditional humanoid robot costs.
+
+## Platform Requirements
+
+<Tip warning={true}>
+  **Linux Only**: OpenArm currently only works on Linux. The CAN bus USB adapter
+  does not have macOS drivers and has not been tested on Windows.
+</Tip>
+
+## Safety Guide
+
+Before operating OpenArm, please read the [official safety guide](https://docs.openarm.dev/getting-started/safety-guide). Key points:
+
+- **Secure installation**: Fasten the arm to a flat, stable surface with screws or clamps
+- **Safe distance**: Keep body parts and objects outside the range of motion during operation
+- **Protective equipment**: Always wear safety goggles; use additional PPE as needed
+- **Payload limits**: Do not exceed specified payload limits (6.0kg peak / 4.1kg nominal per arm)
+- **Emergency stop**: Know the location and operation of the emergency stop device
+- **Regular inspection**: Check for loose screws, damaged mechanical limits, unusual noises, and wiring damage
+
+## Hardware Setup
+
+Follow the official [OpenArm hardware documentation](https://docs.openarm.dev) for:
+
+- Bill of materials and sourcing
+- 3D printing instructions
+- Mechanical assembly
+- Electrical wiring
+
+The hardware repositories are available at [github.com/enactic/openarm](https://github.com/enactic/openarm).
+
+## CAN Bus Setup
+
+OpenArm uses CAN bus communication with Damiao motors. Once you have the CAN bus USB adapter plugged into your Linux PC, follow the [Damiao Motors and CAN Bus guide](./damiao) to configure the interface.
+
+Quick setup:
+
+```bash
+# Setup CAN interfaces
+lerobot-setup-can --mode=setup --interfaces=can0,can1
+
+# Test motor communication
+lerobot-setup-can --mode=test --interfaces=can0,can1
+```
+
+## Install LeRobot 🤗
+
+Follow our [Installation Guide](./installation), then install the Damiao motor support:
+
+```bash
+pip install -e ".[damiao]"
+```
+
+## Usage
+
+### Follower Arm (Robot)
+
+<hfoptions id="follower">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --robot.type=openarm_follower \
+    --robot.port=can0 \
+    --robot.side=right \
+    --robot.id=my_openarm_follower
+```
+
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig
+
+config = OpenArmFollowerConfig(
+    port="can0",
+    side="right",  # or "left" for left arm
+    id="my_openarm_follower",
+)
+
+follower = OpenArmFollower(config)
+follower.connect()
+
+# Read current state
+obs = follower.get_observation()
+print(obs)
+
+# Send action (position in degrees)
+action = {
+    "joint_1.pos": 0.0,
+    "joint_2.pos": 0.0,
+    "joint_3.pos": 0.0,
+    "joint_4.pos": 45.0,
+    "joint_5.pos": 0.0,
+    "joint_6.pos": 0.0,
+    "joint_7.pos": 0.0,
+    "gripper.pos": 0.0,
+}
+follower.send_action(action)
+
+follower.disconnect()
+```
+
+</hfoption>
+</hfoptions>
+
+### Leader Arm (Teleoperator)
+
+The leader arm is used for teleoperation - manually moving it to control the follower arm.
+
+<hfoptions id="leader">
+<hfoption id="Command">
+
+```bash
+lerobot-calibrate \
+    --teleop.type=openarm_leader \
+    --teleop.port=can1 \
+    --teleop.id=my_openarm_leader
+```
+
+</hfoption>
+<hfoption id="API example">
+
+```python
+from lerobot.teleoperators.openarm_leader import OpenArmLeader, OpenArmLeaderConfig
+
+config = OpenArmLeaderConfig(
+    port="can1",
+    id="my_openarm_leader",
+    manual_control=True,  # Disable torque for manual movement
+)
+
+leader = OpenArmLeader(config)
+leader.connect()
+
+# Read current position (as action to send to follower)
+action = leader.get_action()
+print(action)
+
+leader.disconnect()
+```
+
+</hfoption>
+</hfoptions>
+
+### Teleoperation
+
+To teleoperate OpenArm with leader-follower control:
+
+```bash
+lerobot-teleoperate \
+    --robot.type=openarm_follower \
+    --robot.port=can0 \
+    --robot.side=right \
+    --robot.id=my_follower \
+    --teleop.type=openarm_leader \
+    --teleop.port=can1 \
+    --teleop.id=my_leader
+```
+
+### Bimanual Teleoperation
+
+To teleoperate a bimanual OpenArm setup with two leader and two follower arms:
+
+```bash
+lerobot-teleoperate \
+    --robot.type=bi_openarm_follower \
+    --robot.left_arm_config.port=can0 \
+    --robot.left_arm_config.side=left \
+    --robot.right_arm_config.port=can1 \
+    --robot.right_arm_config.side=right \
+    --robot.id=my_bimanual_follower \
+    --teleop.type=bi_openarm_leader \
+    --teleop.left_arm_config.port=can2 \
+    --teleop.right_arm_config.port=can3 \
+    --teleop.id=my_bimanual_leader
+```
+
+### Recording Data
+
+To record a dataset during teleoperation:
+
+```bash
+lerobot-record \
+    --robot.type=openarm_follower \
+    --robot.port=can0 \
+    --robot.side=right \
+    --robot.id=my_follower \
+    --teleop.type=openarm_leader \
+    --teleop.port=can1 \
+    --teleop.id=my_leader \
+    --repo-id=my_hf_username/my_openarm_dataset \
+    --fps=30 \
+    --num-episodes=10
+```
+
+## Configuration Options
+
+### Follower Configuration
+
+| Parameter             | Default   | Description                                                |
+| --------------------- | --------- | ---------------------------------------------------------- |
+| `port`                | -         | CAN interface (e.g., `can0`)                               |
+| `side`                | `None`    | Arm side: `"left"`, `"right"`, or `None` for custom limits |
+| `use_can_fd`          | `True`    | Enable CAN FD for higher data rates                        |
+| `can_bitrate`         | `1000000` | Nominal bitrate (1 Mbps)                                   |
+| `can_data_bitrate`    | `5000000` | CAN FD data bitrate (5 Mbps)                               |
+| `max_relative_target` | `None`    | Safety limit for relative target positions                 |
+| `position_kp`         | Per-joint | Position control proportional gains                        |
+| `position_kd`         | Per-joint | Position control derivative gains                          |
+
+### Leader Configuration
+
+| Parameter          | Default   | Description                         |
+| ------------------ | --------- | ----------------------------------- |
+| `port`             | -         | CAN interface (e.g., `can1`)        |
+| `manual_control`   | `True`    | Disable torque for manual movement  |
+| `use_can_fd`       | `True`    | Enable CAN FD for higher data rates |
+| `can_bitrate`      | `1000000` | Nominal bitrate (1 Mbps)            |
+| `can_data_bitrate` | `5000000` | CAN FD data bitrate (5 Mbps)        |
+
+## Motor Configuration
+
+OpenArm uses Damiao motors with the following default configuration:
+
+| Joint                       | Motor Type | Send ID | Recv ID |
+| --------------------------- | ---------- | ------- | ------- |
+| joint_1 (Shoulder pan)      | DM8009     | 0x01    | 0x11    |
+| joint_2 (Shoulder lift)     | DM8009     | 0x02    | 0x12    |
+| joint_3 (Shoulder rotation) | DM4340     | 0x03    | 0x13    |
+| joint_4 (Elbow flex)        | DM4340     | 0x04    | 0x14    |
+| joint_5 (Wrist roll)        | DM4310     | 0x05    | 0x15    |
+| joint_6 (Wrist pitch)       | DM4310     | 0x06    | 0x16    |
+| joint_7 (Wrist rotation)    | DM4310     | 0x07    | 0x17    |
+| gripper                     | DM4310     | 0x08    | 0x18    |
+
+## Troubleshooting
+
+### No Response from Motors
+
+1. Check power supply connections
+2. Verify CAN wiring (CAN-H, CAN-L, GND)
+3. Run diagnostics: `lerobot-setup-can --mode=test --interfaces=can0`
+4. See the [Damiao troubleshooting guide](./damiao#troubleshooting) for more details
+
+### CAN Interface Not Found
+
+Ensure the CAN interface is configured:
+
+```bash
+ip link show can0
+```
+
+## Resources
+
+- [OpenArm Website](https://openarm.dev)
+- [OpenArm Documentation](https://docs.openarm.dev)
+- [OpenArm GitHub](https://github.com/enactic/openarm)
+- [Safety Guide](https://docs.openarm.dev/getting-started/safety-guide)
+- [Damiao Motors and CAN Bus](./damiao)

docs/source/unitree_g1.mdx

@@ -188,7 +188,105 @@ Press `Ctrl+C` to stop the policy.
 
 ## Running in Simulation Mode (MuJoCo)
 
-You can now test policies before unleashing them on the physical robot using MuJoCo. To do so simply set `is_simulation=True` in config.
+You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI.
+
+### Calibrate Exoskeleton Teleoperator
+
+```bash
+lerobot-calibrate \
+    --teleop.type=unitree_g1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.id=exo
+```
+
+### Teleoperate in Simulation
+
+```bash
+lerobot-teleoperate \
+    --robot.type=unitree_g1 \
+    --robot.is_simulation=true \
+    --teleop.type=unitree_g1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.id=exo \
+    --fps=100
+```
+
+### Record Dataset in Simulation
+
+```bash
+python -m lerobot.scripts.lerobot_record \
+    --robot.type=unitree_g1 \
+    --robot.is_simulation=true \
+    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+    --teleop.type=unitree_g1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.id=exo \
+    --dataset.repo_id=your-username/dataset-name \
+    --dataset.single_task="Test" \
+    --dataset.num_episodes=2 \
+    --dataset.episode_time_s=5 \
+    --dataset.reset_time_s=5 \
+    --dataset.push_to_hub=true
+```
+
+Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim)
+
+---
+
+## Running on Real Robot
+
+Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot.
+
+### Start the Camera Server
+
+On the robot, start the ZMQ image server:
+
+```bash
+python src/lerobot/cameras/zmq/image_server.py
+```
+
+Keep this running in a separate terminal for camera streaming during recording.
+
+### Teleoperate Real Robot
+
+```bash
+lerobot-teleoperate \
+    --robot.type=unitree_g1 \
+    --robot.is_simulation=false \
+    --teleop.type=unitree_g1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.id=exo \
+    --fps=100
+```
+
+### Record Dataset on Real Robot
+
+```bash
+python -m lerobot.scripts.lerobot_record \
+    --robot.type=unitree_g1 \
+    --robot.is_simulation=false \
+    --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \
+    --teleop.type=unitree_g1 \
+    --teleop.left_arm_config.port=/dev/ttyACM1 \
+    --teleop.right_arm_config.port=/dev/ttyACM0 \
+    --teleop.id=exo \
+    --dataset.repo_id=your-username/dataset-name \
+    --dataset.single_task="Test" \
+    --dataset.num_episodes=2 \
+    --dataset.episode_time_s=5 \
+    --dataset.reset_time_s=5 \
+    --dataset.push_to_hub=true
+```
+
+**Note**: Update `server_address` to match your robot's camera server IP.
+
+Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real)
+
+---
 
 ## Additional Resources
 

examples/backward_compatibility/replay.py

@@ -81,24 +81,25 @@ def replay(cfg: ReplayConfig):
     actions = dataset.hf_dataset.select_columns(ACTION)
     robot.connect()
 
-    log_say("Replaying episode", cfg.play_sounds, blocking=True)
-    for idx in range(dataset.num_frames):
-        start_episode_t = time.perf_counter()
+    try:
+        log_say("Replaying episode", cfg.play_sounds, blocking=True)
+        for idx in range(dataset.num_frames):
+            start_episode_t = time.perf_counter()
 
-        action_array = actions[idx][ACTION]
-        action = {}
-        for i, name in enumerate(dataset.features[ACTION]["names"]):
-            key = f"{name.removeprefix('main_')}.pos"
-            action[key] = action_array[i].item()
+            action_array = actions[idx][ACTION]
+            action = {}
+            for i, name in enumerate(dataset.features[ACTION]["names"]):
+                key = f"{name.removeprefix('main_')}.pos"
+                action[key] = action_array[i].item()
 
-        action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
-        action["elbow_flex.pos"] -= 90
-        robot.send_action(action)
+            action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90)
+            action["elbow_flex.pos"] -= 90
+            robot.send_action(action)
 
-        dt_s = time.perf_counter() - start_episode_t
-        precise_sleep(max(1 / dataset.fps - dt_s, 0.0))
-
-    robot.disconnect()
+            dt_s = time.perf_counter() - start_episode_t
+            precise_sleep(max(1 / dataset.fps - dt_s, 0.0))
+    finally:
+        robot.disconnect()
 
 
 if __name__ == "__main__":

examples/lekiwi/evaluate.py

@@ -78,40 +78,24 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="lekiwi_evaluate")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting evaluate loop...")
-    recorded_episodes = 0
-    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
+        print("Starting evaluate loop...")
+        recorded_episodes = 0
+        while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}")
 
-        # Main record loop
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=teleop_action_processor,
-            robot_action_processor=robot_action_processor,
-            robot_observation_processor=robot_observation_processor,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (
-            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-        ):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
+                policy=policy,
+                preprocessor=preprocessor,  # Pass the pre and post policy processors
+                postprocessor=postprocessor,
+                dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
@@ -120,24 +104,42 @@ def main():
                 robot_observation_processor=robot_observation_processor,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=robot,
+                    events=events,
+                    fps=FPS,
+                    control_time_s=EPISODE_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=teleop_action_processor,
+                    robot_action_processor=robot_action_processor,
+                    robot_observation_processor=robot_observation_processor,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        recorded_episodes += 1
+            if events["rerecord_episode"]:
+                log_say("Re-record episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            recorded_episodes += 1
 
-    dataset.finalize()
-    dataset.push_to_hub()
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        robot.disconnect()
+        listener.stop()
+
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/lekiwi/record.py

@@ -74,40 +74,23 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="lekiwi_record")
 
-    if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
+    try:
+        if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected:
+            raise ValueError("Robot or teleop is not connected!")
 
-    print("Starting record loop...")
-    recorded_episodes = 0
-    while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {recorded_episodes}")
+        print("Starting record loop...")
+        recorded_episodes = 0
+        while recorded_episodes < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Recording episode {recorded_episodes}")
 
-        # Main record loop
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            dataset=dataset,
-            teleop=[leader_arm, keyboard],
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=teleop_action_processor,
-            robot_action_processor=robot_action_processor,
-            robot_observation_processor=robot_observation_processor,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (
-            (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
-        ):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
+                dataset=dataset,
                 teleop=[leader_arm, keyboard],
-                control_time_s=RESET_TIME_SEC,
+                control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
                 teleop_action_processor=teleop_action_processor,
@@ -115,26 +98,44 @@ def main():
                 robot_observation_processor=robot_observation_processor,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=robot,
+                    events=events,
+                    fps=FPS,
+                    teleop=[leader_arm, keyboard],
+                    control_time_s=RESET_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=teleop_action_processor,
+                    robot_action_processor=robot_action_processor,
+                    robot_observation_processor=robot_observation_processor,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        recorded_episodes += 1
+            if events["rerecord_episode"]:
+                log_say("Re-record episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    leader_arm.disconnect()
-    keyboard.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            recorded_episodes += 1
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        robot.disconnect()
+        leader_arm.disconnect()
+        keyboard.disconnect()
+        listener.stop()
 
-    dataset.finalize()
-    dataset.push_to_hub()
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/lekiwi/replay.py

@@ -42,25 +42,27 @@ def main():
     # Connect to the robot
     robot.connect()
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+        print("Starting replay loop...")
+        log_say(f"Replaying episode {EPISODE_IDX}")
+        for idx in range(len(episode_frames)):
+            t0 = time.perf_counter()
 
-        # Get recorded action from dataset
-        action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+            # Get recorded action from dataset
+            action = {
+                name: float(actions[idx][ACTION][i])
+                for i, name in enumerate(dataset.features[ACTION]["names"])
+            }
 
-        # Send action to robot
-        _ = robot.send_action(action)
+            # Send action to robot
+            _ = robot.send_action(action)
 
-        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-
-    robot.disconnect()
+            precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+    finally:
+        robot.disconnect()
 
 
 if __name__ == "__main__":

examples/phone_to_so100/evaluate.py

@@ -142,38 +142,24 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="phone_so100_evaluate")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting evaluate loop...")
-    episode_idx = 0
-    for episode_idx in range(NUM_EPISODES):
-        log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+        print("Starting evaluate loop...")
+        episode_idx = 0
+        for episode_idx in range(NUM_EPISODES):
+            log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-        # Main record loop
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=make_default_teleop_action_processor(),
-            robot_action_processor=robot_ee_to_joints_processor,
-            robot_observation_processor=robot_joints_to_ee_pose_processor,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
+                policy=policy,
+                preprocessor=preprocessor,  # Pass the pre and post policy processors
+                postprocessor=postprocessor,
+                dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
@@ -182,24 +168,41 @@ def main():
                 robot_observation_processor=robot_joints_to_ee_pose_processor,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=robot,
+                    events=events,
+                    fps=FPS,
+                    control_time_s=EPISODE_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=make_default_teleop_action_processor(),
+                    robot_action_processor=robot_ee_to_joints_processor,
+                    robot_observation_processor=robot_joints_to_ee_pose_processor,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+            if events["rerecord_episode"]:
+                log_say("Re-record episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            episode_idx += 1
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        robot.disconnect()
+        listener.stop()
 
-    dataset.finalize()
-    dataset.push_to_hub()
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/phone_to_so100/record.py

@@ -149,38 +149,23 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="phone_so100_record")
 
-    if not robot.is_connected or not phone.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
+    try:
+        if not robot.is_connected or not phone.is_connected:
+            raise ValueError("Robot or teleop is not connected!")
 
-    print("Starting record loop. Move your phone to teleoperate the robot...")
-    episode_idx = 0
-    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+        print("Starting record loop. Move your phone to teleoperate the robot...")
+        episode_idx = 0
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
 
-        # Main record loop
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            teleop=phone,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=phone_to_robot_ee_pose_processor,
-            robot_action_processor=robot_ee_to_joints_processor,
-            robot_observation_processor=robot_joints_to_ee_pose,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
                 teleop=phone,
-                control_time_s=RESET_TIME_SEC,
+                dataset=dataset,
+                control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
                 teleop_action_processor=phone_to_robot_ee_pose_processor,
@@ -188,25 +173,43 @@ def main():
                 robot_observation_processor=robot_joints_to_ee_pose,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-recording episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=robot,
+                    events=events,
+                    fps=FPS,
+                    teleop=phone,
+                    control_time_s=RESET_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=phone_to_robot_ee_pose_processor,
+                    robot_action_processor=robot_ee_to_joints_processor,
+                    robot_observation_processor=robot_joints_to_ee_pose,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    phone.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            episode_idx += 1
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        robot.disconnect()
+        phone.disconnect()
+        listener.stop()
 
-    dataset.finalize()
-    dataset.push_to_hub()
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/phone_to_so100/replay.py

@@ -73,32 +73,34 @@ def main():
     # Connect to the robot
     robot.connect()
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+        print("Starting replay loop...")
+        log_say(f"Replaying episode {EPISODE_IDX}")
+        for idx in range(len(episode_frames)):
+            t0 = time.perf_counter()
 
-        # Get recorded action from dataset
-        ee_action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+            # Get recorded action from dataset
+            ee_action = {
+                name: float(actions[idx][ACTION][i])
+                for i, name in enumerate(dataset.features[ACTION]["names"])
+            }
 
-        # Get robot observation
-        robot_obs = robot.get_observation()
+            # Get robot observation
+            robot_obs = robot.get_observation()
 
-        # Dataset EE -> robot joints
-        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+            # Dataset EE -> robot joints
+            joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-        # Send action to robot
-        _ = robot.send_action(joint_action)
+            # Send action to robot
+            _ = robot.send_action(joint_action)
 
-        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
-
-    # Clean up
-    robot.disconnect()
+            precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+    finally:
+        # Clean up
+        robot.disconnect()
 
 
 if __name__ == "__main__":

examples/so100_to_so100_EE/evaluate.py

@@ -142,38 +142,24 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="so100_so100_evaluate")
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting evaluate loop...")
-    episode_idx = 0
-    for episode_idx in range(NUM_EPISODES):
-        log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
+        print("Starting evaluate loop...")
+        episode_idx = 0
+        for episode_idx in range(NUM_EPISODES):
+            log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}")
 
-        # Main record loop
-        record_loop(
-            robot=robot,
-            events=events,
-            fps=FPS,
-            policy=policy,
-            preprocessor=preprocessor,  # Pass the pre and post policy processors
-            postprocessor=postprocessor,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=make_default_teleop_action_processor(),
-            robot_action_processor=robot_ee_to_joints_processor,
-            robot_observation_processor=robot_joints_to_ee_pose_processor,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=robot,
                 events=events,
                 fps=FPS,
+                policy=policy,
+                preprocessor=preprocessor,  # Pass the pre and post policy processors
+                postprocessor=postprocessor,
+                dataset=dataset,
                 control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
@@ -182,24 +168,41 @@ def main():
                 robot_observation_processor=robot_joints_to_ee_pose_processor,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-record episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=robot,
+                    events=events,
+                    fps=FPS,
+                    control_time_s=EPISODE_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=make_default_teleop_action_processor(),
+                    robot_action_processor=robot_ee_to_joints_processor,
+                    robot_observation_processor=robot_joints_to_ee_pose_processor,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+            if events["rerecord_episode"]:
+                log_say("Re-record episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    robot.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            episode_idx += 1
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        robot.disconnect()
+        listener.stop()
 
-    dataset.finalize()
-    dataset.push_to_hub()
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/so100_to_so100_EE/record.py

@@ -146,38 +146,23 @@ def main():
     listener, events = init_keyboard_listener()
     init_rerun(session_name="recording_phone")
 
-    if not leader.is_connected or not follower.is_connected:
-        raise ValueError("Robot or teleop is not connected!")
+    try:
+        if not leader.is_connected or not follower.is_connected:
+            raise ValueError("Robot or teleop is not connected!")
 
-    print("Starting record loop...")
-    episode_idx = 0
-    while episode_idx < NUM_EPISODES and not events["stop_recording"]:
-        log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
+        print("Starting record loop...")
+        episode_idx = 0
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
 
-        # Main record loop
-        record_loop(
-            robot=follower,
-            events=events,
-            fps=FPS,
-            teleop=leader,
-            dataset=dataset,
-            control_time_s=EPISODE_TIME_SEC,
-            single_task=TASK_DESCRIPTION,
-            display_data=True,
-            teleop_action_processor=leader_joints_to_ee,
-            robot_action_processor=ee_to_follower_joints,
-            robot_observation_processor=follower_joints_to_ee,
-        )
-
-        # Reset the environment if not stopping or re-recording
-        if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]):
-            log_say("Reset the environment")
+            # Main record loop
             record_loop(
                 robot=follower,
                 events=events,
                 fps=FPS,
                 teleop=leader,
-                control_time_s=RESET_TIME_SEC,
+                dataset=dataset,
+                control_time_s=EPISODE_TIME_SEC,
                 single_task=TASK_DESCRIPTION,
                 display_data=True,
                 teleop_action_processor=leader_joints_to_ee,
@@ -185,25 +170,44 @@ def main():
                 robot_observation_processor=follower_joints_to_ee,
             )
 
-        if events["rerecord_episode"]:
-            log_say("Re-recording episode")
-            events["rerecord_episode"] = False
-            events["exit_early"] = False
-            dataset.clear_episode_buffer()
-            continue
+            # Reset the environment if not stopping or re-recording
+            if not events["stop_recording"] and (
+                episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]
+            ):
+                log_say("Reset the environment")
+                record_loop(
+                    robot=follower,
+                    events=events,
+                    fps=FPS,
+                    teleop=leader,
+                    control_time_s=RESET_TIME_SEC,
+                    single_task=TASK_DESCRIPTION,
+                    display_data=True,
+                    teleop_action_processor=leader_joints_to_ee,
+                    robot_action_processor=ee_to_follower_joints,
+                    robot_observation_processor=follower_joints_to_ee,
+                )
 
-        # Save episode
-        dataset.save_episode()
-        episode_idx += 1
+            if events["rerecord_episode"]:
+                log_say("Re-recording episode")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
 
-    # Clean up
-    log_say("Stop recording")
-    leader.disconnect()
-    follower.disconnect()
-    listener.stop()
+            # Save episode
+            dataset.save_episode()
+            episode_idx += 1
 
-    dataset.finalize()
-    dataset.push_to_hub()
+    finally:
+        # Clean up
+        log_say("Stop recording")
+        leader.disconnect()
+        follower.disconnect()
+        listener.stop()
+
+        dataset.finalize()
+        dataset.push_to_hub()
 
 
 if __name__ == "__main__":

examples/so100_to_so100_EE/replay.py

@@ -74,32 +74,35 @@ def main():
     # Connect to the robot
     robot.connect()
 
-    if not robot.is_connected:
-        raise ValueError("Robot is not connected!")
+    try:
+        if not robot.is_connected:
+            raise ValueError("Robot is not connected!")
 
-    print("Starting replay loop...")
-    log_say(f"Replaying episode {EPISODE_IDX}")
-    for idx in range(len(episode_frames)):
-        t0 = time.perf_counter()
+        print("Starting replay loop...")
+        log_say(f"Replaying episode {EPISODE_IDX}")
+        for idx in range(len(episode_frames)):
+            t0 = time.perf_counter()
 
-        # Get recorded action from dataset
-        ee_action = {
-            name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"])
-        }
+            # Get recorded action from dataset
+            ee_action = {
+                name: float(actions[idx][ACTION][i])
+                for i, name in enumerate(dataset.features[ACTION]["names"])
+            }
 
-        # Get robot observation
-        robot_obs = robot.get_observation()
+            # Get robot observation
+            robot_obs = robot.get_observation()
 
-        # Dataset EE -> robot joints
-        joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
+            # Dataset EE -> robot joints
+            joint_action = robot_ee_to_joints_processor((ee_action, robot_obs))
 
-        # Send action to robot
-        _ = robot.send_action(joint_action)
+            # Send action to robot
+            _ = robot.send_action(joint_action)
 
-        precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
+            precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0))
 
-    # Clean up
-    robot.disconnect()
+    finally:
+        # Clean up
+        robot.disconnect()
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -105,12 +105,17 @@ dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"]
 damiao = ["python-can>=4.2.0,<5.0.0"]
 
 # Robots
+openarms = ["lerobot[damiao]"]
 gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"]
 hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"]
 lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"]
 unitree_g1 = [
     "pyzmq>=26.2.1,<28.0.0",
-    "onnxruntime>=1.16.0,<2.0.0"
+    "onnxruntime>=1.16.0,<2.0.0",
+    "pin>=3.0.0,<4.0.0",
+    "meshcat>=0.3.0,<0.4.0",
+    "matplotlib>=3.9.0,<4.0.0",
+    "casadi>=3.6.0,<4.0.0",
 ]
 reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
@@ -355,9 +360,9 @@ ignore_errors = false
 module = "lerobot.cameras.*"
 ignore_errors = false
 
-# [[tool.mypy.overrides]]
-# module = "lerobot.motors.*"
-# ignore_errors = false
+[[tool.mypy.overrides]]
+module = "lerobot.motors.*"
+ignore_errors = false
 
 # [[tool.mypy.overrides]]
 # module = "lerobot.robots.*"

src/lerobot/cameras/__init__.py

@@ -13,5 +13,5 @@
 # limitations under the License.
 
 from .camera import Camera
-from .configs import CameraConfig, ColorMode, Cv2Rotation
+from .configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
 from .utils import make_cameras_from_configs

src/lerobot/cameras/camera.py

@@ -15,11 +15,12 @@
 # limitations under the License.
 
 import abc
+import warnings
 from typing import Any
 
 from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
 
-from .configs import CameraConfig, ColorMode
+from .configs import CameraConfig
 
 
 class Camera(abc.ABC):
@@ -30,20 +31,12 @@ class Camera(abc.ABC):
 
     Manages basic camera properties (FPS, resolution) and core operations:
     - Connection/disconnection
-    - Frame capture (sync/async)
+    - Frame capture (sync/async/latest)
 
     Attributes:
         fps (int | None): Configured frames per second
         width (int | None): Frame width in pixels
         height (int | None): Frame height in pixels
-
-    Example:
-        class MyCamera(Camera):
-            def __init__(self, config): ...
-            @property
-            def is_connected(self) -> bool: ...
-            def connect(self, warmup=True): ...
-            # Plus other required methods
     """
 
     def __init__(self, config: CameraConfig):
@@ -56,6 +49,32 @@ class Camera(abc.ABC):
         self.width: int | None = config.width
         self.height: int | None = config.height
 
+    def __enter__(self):
+        """
+        Context manager entry.
+        Automatically connects to the camera.
+        """
+        self.connect()
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback) -> None:
+        """
+        Context manager exit.
+        Automatically disconnects, ensuring resources are released even on error.
+        """
+        self.disconnect()
+
+    def __del__(self) -> None:
+        """
+        Destructor safety net.
+        Attempts to disconnect if the object is garbage collected without cleanup.
+        """
+        try:
+            if self.is_connected:
+                self.disconnect()
+        except Exception:  # nosec B110
+            pass
+
     @property
     @abc.abstractmethod
     def is_connected(self) -> bool:
@@ -89,12 +108,10 @@ class Camera(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
-        """Capture and return a single frame from the camera.
+    def read(self) -> NDArray[Any]:
+        """Capture and return a single frame from the camera synchronously.
 
-        Args:
-            color_mode: Desired color mode for the output frame. If None,
-                        uses the camera's default color mode.
+        This is a blocking call that will wait for the hardware and its SDK.
 
         Returns:
             np.ndarray: Captured frame as a numpy array.
@@ -103,17 +120,64 @@ class Camera(abc.ABC):
 
     @abc.abstractmethod
     def async_read(self, timeout_ms: float = ...) -> NDArray[Any]:
-        """Asynchronously capture and return a single frame from the camera.
+        """Return the most recent new frame.
+
+        This method retrieves the latest frame captured by the background thread.
+        If a new frame is already available in the buffer (captured since the last call),
+        it returns it immediately.
+
+        It blocks up to `timeout_ms` only if the buffer is empty or if the latest frame
+        was already consumed by a previous `async_read` call.
+
+        Essentially, this method return the latest unconsumed frame, waiting if necessary
+        for a new one to arrive within the specified timeout.
+
+        Usage:
+            - Ideal for control loops where you want to ensure every processed frame
+            is fresh, effectively synchronizing your loop to the camera's FPS.
+            - Causes of a timeout usually include: very low camera FPS, heavy processing load,
+            or if the camera is disconnected.
 
         Args:
-            timeout_ms: Maximum time to wait for a frame in milliseconds.
-                        Defaults to implementation-specific timeout.
+            timeout_ms: Maximum time to wait for a new frame in milliseconds.
+                        Defaults to 200ms (0.2s).
 
         Returns:
             np.ndarray: Captured frame as a numpy array.
+
+        Raises:
+            TimeoutError: If no new frame arrives within `timeout_ms`.
         """
         pass
 
+    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+        """Return the most recent frame captured immediately (Peeking).
+
+        This method is non-blocking and returns whatever is currently in the
+        memory buffer. The frame may be stale,
+        meaning it could have been captured a while ago (hanging camera scenario e.g.).
+
+        Usage:
+            Ideal for scenarios requiring zero latency or decoupled frequencies & when
+            we want a guaranteed frame, such as UI visualization, logging, or
+            non-critical monitoring.
+
+        Returns:
+            NDArray[Any]: The frame image (numpy array).
+
+        Raises:
+            TimeoutError: If the latest frame is older than `max_age_ms`.
+            NotConnectedError: If the camera is not connected.
+            RuntimeError: If the camera is connected but has not captured any frames yet.
+        """
+        warnings.warn(
+            f"{self.__class__.__name__}.read_latest() is not implemented. "
+            "Please override read_latest(); it will be required in future releases.",
+            FutureWarning,
+            stacklevel=2,
+        )
+        return self.async_read()
+
     @abc.abstractmethod
     def disconnect(self) -> None:
         """Disconnect from the camera and release resources."""

src/lerobot/cameras/configs.py

@@ -25,6 +25,10 @@ class ColorMode(str, Enum):
     RGB = "rgb"
     BGR = "bgr"
 
+    @classmethod
+    def _missing_(cls, value: object) -> None:
+        raise ValueError(f"`color_mode` is expected to be in {list(cls)}, but {value} is provided.")
+
 
 class Cv2Rotation(int, Enum):
     NO_ROTATION = 0
@@ -32,6 +36,25 @@ class Cv2Rotation(int, Enum):
     ROTATE_180 = 180
     ROTATE_270 = -90
 
+    @classmethod
+    def _missing_(cls, value: object) -> None:
+        raise ValueError(f"`rotation` is expected to be in {list(cls)}, but {value} is provided.")
+
+
+# Subset from https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html
+class Cv2Backends(int, Enum):
+    ANY = 0
+    V4L2 = 200
+    DSHOW = 700
+    PVAPI = 800
+    ANDROID = 1000
+    AVFOUNDATION = 1200
+    MSMF = 1400
+
+    @classmethod
+    def _missing_(cls, value: object) -> None:
+        raise ValueError(f"`backend` is expected to be in {list(cls)}, but {value} is provided.")
+
 
 @dataclass(kw_only=True)
 class CameraConfig(draccus.ChoiceRegistry, abc.ABC):  # type: ignore  # TODO: add type stubs for draccus

src/lerobot/cameras/opencv/camera_opencv.py

@@ -32,10 +32,11 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
     os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
-from ..utils import get_cv2_backend, get_cv2_rotation
+from ..utils import get_cv2_rotation
 from .configuration_opencv import ColorMode, OpenCVCameraConfig
 
 # NOTE(Steven): The maximum opencv device index depends on your operating system. For instance,
@@ -70,34 +71,24 @@ class OpenCVCamera(Camera):
     Example:
         ```python
         from lerobot.cameras.opencv import OpenCVCamera
-        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation
+        from lerobot.cameras.configuration_opencv import OpenCVCameraConfig
 
         # Basic usage with camera index 0
         config = OpenCVCameraConfig(index_or_path=0)
         camera = OpenCVCamera(config)
         camera.connect()
 
-        # Read 1 frame synchronously
+        # Read 1 frame synchronously (blocking)
         color_image = camera.read()
-        print(color_image.shape)
 
-        # Read 1 frame asynchronously
+        # Read 1 frame asynchronously (waits for new frame with a timeout)
         async_image = camera.async_read()
 
+        # Get the latest frame immediately (no wait, returns timestamp)
+        latest_image, timestamp = camera.read_latest()
+
         # When done, properly disconnect the camera using
         camera.disconnect()
-
-        # Example with custom settings
-        custom_config = OpenCVCameraConfig(
-            index_or_path='/dev/video0', # Or use an index
-            fps=30,
-            width=1280,
-            height=720,
-            color_mode=ColorMode.RGB,
-            rotation=Cv2Rotation.ROTATE_90
-        )
-        custom_camera = OpenCVCamera(custom_config)
-        # ... connect, read, disconnect ...
         ```
     """
 
@@ -123,10 +114,11 @@ class OpenCVCamera(Camera):
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
         self.latest_frame: NDArray[Any] | None = None
+        self.latest_timestamp: float | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
-        self.backend: int = get_cv2_backend()
+        self.backend: int = config.backend
 
         if self.height and self.width:
             self.capture_width, self.capture_height = self.width, self.height
@@ -141,20 +133,23 @@ class OpenCVCamera(Camera):
         """Checks if the camera is currently connected and opened."""
         return isinstance(self.videocapture, cv2.VideoCapture) and self.videocapture.isOpened()
 
+    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the OpenCV camera specified in the configuration.
 
         Initializes the OpenCV VideoCapture object, sets desired camera properties
-        (FPS, width, height), and performs initial checks.
+        (FPS, width, height), starts the background reading thread and performs initial checks.
+
+        Args:
+            warmup (bool): If True, waits at connect() time until at least one valid frame
+                           has been captured by the background thread. Defaults to True.
 
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
-            ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open.
-            RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings.
+            ConnectionError: If the specified camera index/path is not found or fails to open.
+            RuntimeError: If the camera opens but fails to apply requested settings.
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         # Use 1 thread for OpenCV operations to avoid potential conflicts or
         # blocking in multi-threaded applications, especially during data collection.
@@ -170,15 +165,20 @@ class OpenCVCamera(Camera):
             )
 
         self._configure_capture_settings()
+        self._start_read_thread()
 
-        if warmup:
+        if warmup and self.warmup_s > 0:
             start_time = time.time()
             while time.time() - start_time < self.warmup_s:
-                self.read()
+                self.async_read(timeout_ms=self.warmup_s * 1000)
                 time.sleep(0.1)
+            with self.frame_lock:
+                if self.latest_frame is None:
+                    raise ConnectionError(f"{self} failed to capture frames during warmup.")
 
         logger.info(f"{self} connected.")
 
+    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """
         Applies the specified FOURCC, FPS, width, and height settings to the connected camera.
@@ -196,11 +196,8 @@ class OpenCVCamera(Camera):
         Raises:
             RuntimeError: If the camera fails to set any of the specified properties
                           to the requested value.
-            DeviceNotConnectedError: If the camera is not connected when attempting
-                                     to configure settings.
+            DeviceNotConnectedError: If the camera is not connected.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.")
 
         # Set FOURCC first (if specified) as it can affect available FPS/resolution options
         if self.config.fourcc is not None:
@@ -339,6 +336,18 @@ class OpenCVCamera(Camera):
 
         return found_cameras_info
 
+    def _read_from_hardware(self) -> NDArray[Any]:
+        if self.videocapture is None:
+            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+
+        ret, frame = self.videocapture.read()
+
+        if not ret:
+            raise RuntimeError(f"{self} read failed (status={ret}).")
+
+        return frame
+
+    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
@@ -346,11 +355,6 @@ class OpenCVCamera(Camera):
         This is a blocking call. It waits for the next available frame from the
         camera hardware via OpenCV.
 
-        Args:
-            color_mode (Optional[ColorMode]): If specified, overrides the default
-                color mode (`self.color_mode`) for this read operation (e.g.,
-                request RGB even if default is BGR).
-
         Returns:
             np.ndarray: The captured frame as a NumPy array in the format
                        (height, width, channels), using the specified or default
@@ -362,34 +366,31 @@ class OpenCVCamera(Camera):
                           received frame dimensions don't match expectations before rotation.
             ValueError: If an invalid `color_mode` is requested.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         start_time = time.perf_counter()
 
-        if self.videocapture is None:
-            raise DeviceNotConnectedError(f"{self} videocapture is not initialized")
+        if color_mode is not None:
+            logger.warning(
+                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
+            )
 
-        ret, frame = self.videocapture.read()
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
 
-        if not ret or frame is None:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
-
-        processed_frame = self._postprocess_image(frame, color_mode)
+        self.new_frame_event.clear()
+        frame = self.async_read(timeout_ms=10000)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
-        return processed_frame
+        return frame
 
-    def _postprocess_image(self, image: NDArray[Any], color_mode: ColorMode | None = None) -> NDArray[Any]:
+    def _postprocess_image(self, image: NDArray[Any]) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw frame.
 
         Args:
             image (np.ndarray): The raw image frame (expected BGR format from OpenCV).
-            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
-                                             uses the instance's default `self.color_mode`.
 
         Returns:
             np.ndarray: The processed image frame.
@@ -399,11 +400,10 @@ class OpenCVCamera(Camera):
             RuntimeError: If the raw frame dimensions do not match the configured
                           `width` and `height`.
         """
-        requested_color_mode = self.color_mode if color_mode is None else color_mode
 
-        if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR):
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
-                f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+                f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
             )
 
         h, w, c = image.shape
@@ -417,7 +417,7 @@ class OpenCVCamera(Camera):
             raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).")
 
         processed_image = image
-        if requested_color_mode == ColorMode.RGB:
+        if self.color_mode == ColorMode.RGB:
             processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
         if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]:
@@ -431,7 +431,7 @@ class OpenCVCamera(Camera):
 
         On each iteration:
         1. Reads a color frame
-        2. Stores result in latest_frame (thread-safe)
+        2. Stores result in latest_frame and updates timestamp (thread-safe)
         3. Sets new_frame_event to notify listeners
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
@@ -439,30 +439,37 @@ class OpenCVCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
+        failure_count = 0
         while not self.stop_event.is_set():
             try:
-                color_image = self.read()
+                raw_frame = self._read_from_hardware()
+                processed_frame = self._postprocess_image(raw_frame)
+                capture_time = time.perf_counter()
 
                 with self.frame_lock:
-                    self.latest_frame = color_image
+                    self.latest_frame = processed_frame
+                    self.latest_timestamp = capture_time
                 self.new_frame_event.set()
+                failure_count = 0
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
-                logger.warning(f"Error reading frame in background thread for {self}: {e}")
+                if failure_count <= 10:
+                    failure_count += 1
+                    logger.warning(f"Error reading frame in background thread for {self}: {e}")
+                else:
+                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
 
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=0.1)
-        if self.stop_event is not None:
-            self.stop_event.set()
+        self._stop_read_thread()
 
         self.stop_event = Event()
         self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
         self.thread.daemon = True
         self.thread.start()
+        time.sleep(0.1)
 
     def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
@@ -475,6 +482,12 @@ class OpenCVCamera(Camera):
         self.thread = None
         self.stop_event = None
 
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
+    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame asynchronously.
@@ -482,6 +495,7 @@ class OpenCVCamera(Camera):
         This method retrieves the most recent frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
         but may wait up to timeout_ms for the background thread to provide a frame.
+        It is “best effort” under high FPS.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
@@ -496,17 +510,14 @@ class OpenCVCamera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
-            self._start_read_thread()
+            raise RuntimeError(f"{self} read thread is not running.")
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
-            thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {thread_alive}."
+                f"Read thread alive: {self.thread.is_alive()}."
             )
 
         with self.frame_lock:
@@ -518,6 +529,41 @@ class OpenCVCamera(Camera):
 
         return frame
 
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+        """Return the most recent frame captured immediately (Peeking).
+
+        This method is non-blocking and returns whatever is currently in the
+        memory buffer. The frame may be stale,
+        meaning it could have been captured a while ago (hanging camera scenario e.g.).
+
+        Returns:
+            NDArray[Any]: The frame image (numpy array).
+
+        Raises:
+            TimeoutError: If the latest frame is older than `max_age_ms`.
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If the camera is connected but has not captured any frames yet.
+        """
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        with self.frame_lock:
+            frame = self.latest_frame
+            timestamp = self.latest_timestamp
+
+        if frame is None or timestamp is None:
+            raise RuntimeError(f"{self} has not captured any frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return frame
+
     def disconnect(self) -> None:
         """
         Disconnects from the camera and cleans up resources.
@@ -538,4 +584,9 @@ class OpenCVCamera(Camera):
             self.videocapture.release()
             self.videocapture = None
 
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/opencv/configuration_opencv.py

@@ -15,9 +15,9 @@
 from dataclasses import dataclass
 from pathlib import Path
 
-from ..configs import CameraConfig, ColorMode, Cv2Rotation
+from ..configs import CameraConfig, ColorMode, Cv2Backends, Cv2Rotation
 
-__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation"]
+__all__ = ["OpenCVCameraConfig", "ColorMode", "Cv2Rotation", "Cv2Backends"]
 
 
 @CameraConfig.register_subclass("opencv")
@@ -50,6 +50,7 @@ class OpenCVCameraConfig(CameraConfig):
         rotation: Image rotation setting (0°, 90°, 180°, or 270°). Defaults to no rotation.
         warmup_s: Time reading frames before returning from connect (in seconds)
         fourcc: FOURCC code for video format (e.g., "MJPG", "YUYV", "I420"). Defaults to None (auto-detect).
+        backend: OpenCV backend identifier (https://docs.opencv.org/3.4/d4/d15/group__videoio__flags__base.html). Defaults to ANY.
 
     Note:
         - Only 3-channel color output (RGB/BGR) is currently supported.
@@ -62,22 +63,12 @@ class OpenCVCameraConfig(CameraConfig):
     rotation: Cv2Rotation = Cv2Rotation.NO_ROTATION
     warmup_s: int = 1
     fourcc: str | None = None
+    backend: Cv2Backends = Cv2Backends.ANY
 
     def __post_init__(self) -> None:
-        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
-            raise ValueError(
-                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
-            )
-
-        if self.rotation not in (
-            Cv2Rotation.NO_ROTATION,
-            Cv2Rotation.ROTATE_90,
-            Cv2Rotation.ROTATE_180,
-            Cv2Rotation.ROTATE_270,
-        ):
-            raise ValueError(
-                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
-            )
+        self.color_mode = ColorMode(self.color_mode)
+        self.rotation = Cv2Rotation(self.rotation)
+        self.backend = Cv2Backends(self.backend)
 
         if self.fourcc is not None and (not isinstance(self.fourcc, str) or len(self.fourcc) != 4):
             raise ValueError(

src/lerobot/cameras/reachy2_camera/configuration_reachy2_camera.py

@@ -74,7 +74,4 @@ class Reachy2CameraConfig(CameraConfig):
                 f"`image_type` is expected to be 'left' or 'right' for teleop camera, and 'rgb' or 'depth' for depth camera, but {self.image_type} is provided."
             )
 
-        if self.color_mode not in ["rgb", "bgr"]:
-            raise ValueError(
-                f"`color_mode` is expected to be 'rgb' or 'bgr', but {self.color_mode} is provided."
-            )
+        self.color_mode = ColorMode(self.color_mode)

src/lerobot/cameras/reachy2_camera/reachy2_camera.py

@@ -32,6 +32,7 @@ if platform.system() == "Windows" and "OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"
 import cv2  # type: ignore  # TODO: add type stubs for OpenCV
 import numpy as np  # type: ignore  # TODO: add type stubs for numpy
 
+from lerobot.utils.decorators import check_if_not_connected
 from lerobot.utils.import_utils import _reachy2_sdk_available
 
 if TYPE_CHECKING or _reachy2_sdk_available:
@@ -80,6 +81,8 @@ class Reachy2Camera(Camera):
         self.config = config
 
         self.color_mode = config.color_mode
+        self.latest_frame: NDArray[Any] | None = None
+        self.latest_timestamp: float | None = None
 
         self.cam_manager: CameraManager | None = None
 
@@ -121,16 +124,12 @@ class Reachy2Camera(Camera):
         """
         raise NotImplementedError("Camera detection is not implemented for Reachy2 cameras.")
 
+    @check_if_not_connected
     def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
         Reads a single frame synchronously from the camera.
 
-        This is a blocking call.
-
-        Args:
-            color_mode (Optional[ColorMode]): If specified, overrides the default
-                color mode (`self.color_mode`) for this read operation (e.g.,
-                request RGB even if default is BGR).
+        This method retrieves the most recent frame available in Reachy 2's low-level software.
 
         Returns:
             np.ndarray: The captured frame as a NumPy array in the format
@@ -139,12 +138,14 @@ class Reachy2Camera(Camera):
         """
         start_time = time.perf_counter()
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         if self.cam_manager is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
+        if color_mode is not None:
+            logger.warning(
+                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
+            )
+
         frame: NDArray[Any] = np.empty((0, 0, 3), dtype=np.uint8)
 
         if self.config.name == "teleop" and hasattr(self.cam_manager, "teleop"):
@@ -165,25 +166,27 @@ class Reachy2Camera(Camera):
             raise ValueError(f"Invalid camera name '{self.config.name}'. Expected 'teleop' or 'depth'.")
 
         if frame is None:
-            return np.empty((0, 0, 3), dtype=np.uint8)
+            raise RuntimeError(f"Internal error: No frame available for {self}.")
 
-        if self.config.color_mode == "rgb":
+        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
+            raise ValueError(
+                f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+            )
+        if self.color_mode == ColorMode.RGB:
             frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
+        self.latest_frame = frame
+        self.latest_timestamp = time.perf_counter()
+
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
         return frame
 
+    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
-        Reads the latest available frame.
-
-        This method retrieves the most recent frame available in Reachy 2's low-level software.
-
-        Args:
-            timeout_ms (float): Maximum time in milliseconds to wait for a frame
-                to become available. Defaults to 200ms (0.2 seconds).
+        Same as read()
 
         Returns:
             np.ndarray: The latest captured frame as a NumPy array in the format
@@ -194,16 +197,40 @@ class Reachy2Camera(Camera):
             TimeoutError: If no frame becomes available within the specified timeout.
             RuntimeError: If an unexpected error occurs.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        frame = self.read()
+        return self.read()
 
-        if frame is None:
-            raise RuntimeError(f"Internal error: No frame available for {self}.")
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+        """Return the most recent frame captured immediately (Peeking).
 
-        return frame
+        This method is non-blocking and returns whatever is currently in the
+        memory buffer. The frame may be stale,
+        meaning it could have been captured a while ago (hanging camera scenario e.g.).
 
+        Returns:
+            tuple[NDArray, float]:
+                - The frame image (numpy array).
+                - The timestamp (time.perf_counter) when this frame was captured.
+
+        Raises:
+            TimeoutError: If the latest frame is older than `max_age_ms`.
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If the camera is connected but has not captured any frames yet.
+        """
+
+        if self.latest_frame is None or self.latest_timestamp is None:
+            raise RuntimeError(f"{self} has not captured any frames yet.")
+
+        age_ms = (time.perf_counter() - self.latest_timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return self.latest_frame
+
+    @check_if_not_connected
     def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
@@ -211,8 +238,6 @@ class Reachy2Camera(Camera):
         Raises:
             DeviceNotConnectedError: If the camera is already disconnected.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} not connected.")
 
         if self.cam_manager is not None:
             self.cam_manager.disconnect()

src/lerobot/cameras/realsense/camera_realsense.py

@@ -30,7 +30,8 @@ try:
 except Exception as e:
     logging.info(f"Could not import realsense: {e}")
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -72,15 +73,14 @@ class RealSenseCamera(Camera):
         camera = RealSenseCamera(config)
         camera.connect()
 
-        # Read 1 frame synchronously
+        # Read 1 frame synchronously (blocking)
         color_image = camera.read()
-        print(color_image.shape)
 
-        # Read 1 frame asynchronously
+        # Read 1 frame asynchronously (waits for new frame with a timeout)
         async_image = camera.async_read()
 
-        # When done, properly disconnect the camera using
-        camera.disconnect()
+        # Get the latest frame immediately (no wait, returns timestamp)
+        latest_image, timestamp = camera.read_latest()
 
         # Example with depth capture and custom settings
         custom_config = RealSenseCameraConfig(
@@ -133,7 +133,9 @@ class RealSenseCamera(Camera):
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
-        self.latest_frame: NDArray[Any] | None = None
+        self.latest_color_frame: NDArray[Any] | None = None
+        self.latest_depth_frame: NDArray[Any] | None = None
+        self.latest_timestamp: float | None = None
         self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
@@ -151,6 +153,7 @@ class RealSenseCamera(Camera):
         """Checks if the camera pipeline is started and streams are active."""
         return self.rs_pipeline is not None and self.rs_profile is not None
 
+    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
         """
         Connects to the RealSense camera specified in the configuration.
@@ -158,14 +161,16 @@ class RealSenseCamera(Camera):
         Initializes the RealSense pipeline, configures the required streams (color
         and optionally depth), starts the pipeline, and validates the actual stream settings.
 
+        Args:
+            warmup (bool): If True, waits at connect() time until at least one valid frame
+                           has been captured by the background thread. Defaults to True.
+
         Raises:
             DeviceAlreadyConnectedError: If the camera is already connected.
             ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique).
             ConnectionError: If the camera is found but fails to start the pipeline or no RealSense devices are detected at all.
             RuntimeError: If the pipeline starts but fails to apply requested settings.
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         self.rs_pipeline = rs.pipeline()
         rs_config = rs.config()
@@ -181,15 +186,18 @@ class RealSenseCamera(Camera):
             ) from e
 
         self._configure_capture_settings()
+        self._start_read_thread()
 
-        if warmup:
-            time.sleep(
-                1
-            )  # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise.
-            start_time = time.time()
-            while time.time() - start_time < self.warmup_s:
-                self.read()
-                time.sleep(0.1)
+        # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise.
+        self.warmup_s = max(self.warmup_s, 1)
+
+        start_time = time.time()
+        while time.time() - start_time < self.warmup_s:
+            self.async_read(timeout_ms=self.warmup_s * 1000)
+            time.sleep(0.1)
+        with self.frame_lock:
+            if self.latest_color_frame is None or self.use_depth and self.latest_depth_frame is None:
+                raise ConnectionError(f"{self} failed to capture frames during warmup.")
 
         logger.info(f"{self} connected.")
 
@@ -282,6 +290,7 @@ class RealSenseCamera(Camera):
             if self.use_depth:
                 rs_config.enable_stream(rs.stream.depth)
 
+    @check_if_not_connected
     def _configure_capture_settings(self) -> None:
         """Sets fps, width, and height from device stream if not already configured.
 
@@ -291,8 +300,6 @@ class RealSenseCamera(Camera):
         Raises:
             DeviceNotConnectedError: If device is not connected.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"Cannot validate settings for {self} as it is not connected.")
 
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -312,6 +319,7 @@ class RealSenseCamera(Camera):
                 self.width, self.height = actual_width, actual_height
                 self.capture_width, self.capture_height = actual_width, actual_height
 
+    @check_if_not_connected
     def read_depth(self, timeout_ms: int = 200) -> NDArray[Any]:
         """
         Reads a single frame (depth) synchronously from the camera.
@@ -319,9 +327,6 @@ class RealSenseCamera(Camera):
         This is a blocking call. It waits for a coherent set of frames (depth)
         from the camera hardware via the RealSense pipeline.
 
-        Args:
-            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
-
         Returns:
             np.ndarray: The depth map as a NumPy array (height, width)
                   of type `np.uint16` (raw depth values in millimeters) and rotation.
@@ -330,44 +335,50 @@ class RealSenseCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If reading frames from the pipeline fails or frames are invalid.
         """
+        if timeout_ms:
+            logger.warning(
+                f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
+            )
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
         if not self.use_depth:
             raise RuntimeError(
                 f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
             )
 
-        start_time = time.perf_counter()
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
 
+        self.new_frame_event.clear()
+
+        _ = self.async_read(timeout_ms=10000)
+
+        with self.frame_lock:
+            depth_map = self.latest_depth_frame
+
+        if depth_map is None:
+            raise RuntimeError("No depth frame available. Ensure camera is streaming.")
+
+        return depth_map
+
+    def _read_from_hardware(self):
         if self.rs_pipeline is None:
             raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
 
-        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
+        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=10000)
 
         if not ret or frame is None:
-            raise RuntimeError(f"{self} read_depth failed (status={ret}).")
+            raise RuntimeError(f"{self} read failed (status={ret}).")
 
-        depth_frame = frame.get_depth_frame()
-        depth_map = np.asanyarray(depth_frame.get_data())
+        return frame
 
-        depth_map_processed = self._postprocess_image(depth_map, depth_frame=True)
-
-        read_duration_ms = (time.perf_counter() - start_time) * 1e3
-        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
-
-        return depth_map_processed
-
-    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> NDArray[Any]:
+    @check_if_not_connected
+    def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 0) -> NDArray[Any]:
         """
         Reads a single frame (color) synchronously from the camera.
 
         This is a blocking call. It waits for a coherent set of frames (color)
         from the camera hardware via the RealSense pipeline.
 
-        Args:
-            timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms.
-
         Returns:
             np.ndarray: The captured color frame as a NumPy array
               (height, width, channels), processed according to `color_mode` and rotation.
@@ -378,39 +389,36 @@ class RealSenseCamera(Camera):
             ValueError: If an invalid `color_mode` is requested.
         """
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         start_time = time.perf_counter()
 
-        if self.rs_pipeline is None:
-            raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.")
+        if color_mode is not None:
+            logger.warning(
+                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
+            )
 
-        ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms)
+        if timeout_ms:
+            logger.warning(
+                f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions."
+            )
 
-        if not ret or frame is None:
-            raise RuntimeError(f"{self} read failed (status={ret}).")
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
 
-        color_frame = frame.get_color_frame()
-        color_image_raw = np.asanyarray(color_frame.get_data())
+        self.new_frame_event.clear()
 
-        color_image_processed = self._postprocess_image(color_image_raw, color_mode)
+        frame = self.async_read(timeout_ms=10000)
 
         read_duration_ms = (time.perf_counter() - start_time) * 1e3
         logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
 
-        return color_image_processed
+        return frame
 
-    def _postprocess_image(
-        self, image: NDArray[Any], color_mode: ColorMode | None = None, depth_frame: bool = False
-    ) -> NDArray[Any]:
+    def _postprocess_image(self, image: NDArray[Any], depth_frame: bool = False) -> NDArray[Any]:
         """
         Applies color conversion, dimension validation, and rotation to a raw color frame.
 
         Args:
             image (np.ndarray): The raw image frame (expected RGB format from RealSense).
-            color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None,
-                                             uses the instance's default `self.color_mode`.
 
         Returns:
             np.ndarray: The processed image frame according to `self.color_mode` and `self.rotation`.
@@ -421,9 +429,9 @@ class RealSenseCamera(Camera):
                           `width` and `height`.
         """
 
-        if color_mode and color_mode not in (ColorMode.RGB, ColorMode.BGR):
+        if self.color_mode and self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
             raise ValueError(
-                f"Invalid requested color mode '{color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
+                f"Invalid requested color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}."
             )
 
         if depth_frame:
@@ -454,7 +462,7 @@ class RealSenseCamera(Camera):
 
         On each iteration:
         1. Reads a color frame with 500ms timeout
-        2. Stores result in latest_frame (thread-safe)
+        2. Stores result in latest_frame and updates timestamp (thread-safe)
         3. Sets new_frame_event to notify listeners
 
         Stops on DeviceNotConnectedError, logs other errors and continues.
@@ -462,25 +470,41 @@ class RealSenseCamera(Camera):
         if self.stop_event is None:
             raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.")
 
+        failure_count = 0
         while not self.stop_event.is_set():
             try:
-                color_image = self.read(timeout_ms=500)
+                frame = self._read_from_hardware()
+                color_frame_raw = frame.get_color_frame()
+                color_frame = np.asanyarray(color_frame_raw.get_data())
+                processed_color_frame = self._postprocess_image(color_frame)
+
+                if self.use_depth:
+                    depth_frame_raw = frame.get_depth_frame()
+                    depth_frame = np.asanyarray(depth_frame_raw.get_data())
+                    processed_depth_frame = self._postprocess_image(depth_frame, depth_frame=True)
+
+                capture_time = time.perf_counter()
 
                 with self.frame_lock:
-                    self.latest_frame = color_image
+                    self.latest_color_frame = processed_color_frame
+                    if self.use_depth:
+                        self.latest_depth_frame = processed_depth_frame
+                    self.latest_timestamp = capture_time
                 self.new_frame_event.set()
+                failure_count = 0
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
-                logger.warning(f"Error reading frame in background thread for {self}: {e}")
+                if failure_count <= 10:
+                    failure_count += 1
+                    logger.warning(f"Error reading frame in background thread for {self}: {e}")
+                else:
+                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
 
     def _start_read_thread(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=0.1)
-        if self.stop_event is not None:
-            self.stop_event.set()
+        self._stop_read_thread()
 
         self.stop_event = Event()
         self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
@@ -498,7 +522,14 @@ class RealSenseCamera(Camera):
         self.thread = None
         self.stop_event = None
 
+        with self.frame_lock:
+            self.latest_color_frame = None
+            self.latest_depth_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
     # NOTE(Steven): Missing implementation for depth for now
+    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Reads the latest available frame data (color) asynchronously.
@@ -506,6 +537,7 @@ class RealSenseCamera(Camera):
         This method retrieves the most recent color frame captured by the background
         read thread. It does not block waiting for the camera hardware directly,
         but may wait up to timeout_ms for the background thread to provide a frame.
+        It is “best effort” under high FPS.
 
         Args:
             timeout_ms (float): Maximum time in milliseconds to wait for a frame
@@ -520,21 +552,18 @@ class RealSenseCamera(Camera):
             TimeoutError: If no frame data becomes available within the specified timeout.
             RuntimeError: If the background thread died unexpectedly or another error occurs.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
-            self._start_read_thread()
+            raise RuntimeError(f"{self} read thread is not running.")
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
-            thread_alive = self.thread is not None and self.thread.is_alive()
             raise TimeoutError(
                 f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {thread_alive}."
+                f"Read thread alive: {self.thread.is_alive()}."
             )
 
         with self.frame_lock:
-            frame = self.latest_frame
+            frame = self.latest_color_frame
             self.new_frame_event.clear()
 
         if frame is None:
@@ -542,6 +571,42 @@ class RealSenseCamera(Camera):
 
         return frame
 
+    # NOTE(Steven): Missing implementation for depth for now
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+        """Return the most recent (color) frame captured immediately (Peeking).
+
+        This method is non-blocking and returns whatever is currently in the
+        memory buffer. The frame may be stale,
+        meaning it could have been captured a while ago (hanging camera scenario e.g.).
+
+        Returns:
+            NDArray[Any]: The frame image (numpy array).
+
+        Raises:
+            TimeoutError: If the latest frame is older than `max_age_ms`.
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If the camera is connected but has not captured any frames yet.
+        """
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        with self.frame_lock:
+            frame = self.latest_color_frame
+            timestamp = self.latest_timestamp
+
+        if frame is None or timestamp is None:
+            raise RuntimeError(f"{self} has not captured any frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return frame
+
     def disconnect(self) -> None:
         """
         Disconnects from the camera, stops the pipeline, and cleans up resources.
@@ -565,4 +630,10 @@ class RealSenseCamera(Camera):
             self.rs_pipeline = None
             self.rs_profile = None
 
+        with self.frame_lock:
+            self.latest_color_frame = None
+            self.latest_depth_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/realsense/configuration_realsense.py

@@ -60,20 +60,8 @@ class RealSenseCameraConfig(CameraConfig):
     warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
-            raise ValueError(
-                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
-            )
-
-        if self.rotation not in (
-            Cv2Rotation.NO_ROTATION,
-            Cv2Rotation.ROTATE_90,
-            Cv2Rotation.ROTATE_180,
-            Cv2Rotation.ROTATE_270,
-        ):
-            raise ValueError(
-                f"`rotation` is expected to be in {(Cv2Rotation.NO_ROTATION, Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_180, Cv2Rotation.ROTATE_270)}, but {self.rotation} is provided."
-            )
+        self.color_mode = ColorMode(self.color_mode)
+        self.rotation = Cv2Rotation(self.rotation)
 
         values = (self.fps, self.width, self.height)
         if any(v is not None for v in values) and any(v is None for v in values):

src/lerobot/cameras/utils.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import platform
 from typing import cast
 
 from lerobot.utils.import_utils import make_device_from_device_class
@@ -68,14 +67,3 @@ def get_cv2_rotation(rotation: Cv2Rotation) -> int | None:
         return int(cv2.ROTATE_90_COUNTERCLOCKWISE)
     else:
         return None
-
-
-def get_cv2_backend() -> int:
-    import cv2
-
-    if platform.system() == "Windows":
-        return int(cv2.CAP_MSMF)  # Use MSMF for Windows instead of AVFOUNDATION
-    # elif platform.system() == "Darwin":  # macOS
-    #     return cv2.CAP_AVFOUNDATION
-    else:  # Linux and others
-        return int(cv2.CAP_ANY)

src/lerobot/cameras/zmq/camera_zmq.py

@@ -34,7 +34,8 @@ import cv2
 import numpy as np
 from numpy.typing import NDArray
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.errors import DeviceNotConnectedError
 
 from ..camera import Camera
 from ..configs import ColorMode
@@ -45,6 +46,12 @@ logger = logging.getLogger(__name__)
 
 class ZMQCamera(Camera):
     """
+    Manages camera interactions via ZeroMQ for receiving frames from a remote server.
+
+    This class connects to a ZMQ Publisher, subscribes to frame topics, and decodes
+    incoming JSON messages containing Base64 encoded images. It supports both
+    synchronous and asynchronous frame reading patterns.
+
     Example usage:
         ```python
         from lerobot.cameras.zmq import ZMQCamera, ZMQCameraConfig
@@ -52,7 +59,16 @@ class ZMQCamera(Camera):
         config = ZMQCameraConfig(server_address="192.168.123.164", port=5555, camera_name="head_camera")
         camera = ZMQCamera(config)
         camera.connect()
-        frame = camera.read()
+
+        # Read 1 frame synchronously (blocking)
+        color_image = camera.read()
+
+        # Read 1 frame asynchronously (waits for new frame with a timeout)
+        async_image = camera.async_read()
+
+        # Get the latest frame immediately (no wait, returns timestamp)
+        latest_image, timestamp = camera.read_latest()
+
         camera.disconnect()
         ```
     """
@@ -68,14 +84,17 @@ class ZMQCamera(Camera):
         self.color_mode = config.color_mode
         self.timeout_ms = config.timeout_ms
 
+        # ZMQ Context and Socket
         self.context: zmq.Context | None = None
         self.socket: zmq.Socket | None = None
         self._connected = False
 
+        # Threading resources
         self.thread: Thread | None = None
         self.stop_event: Event | None = None
         self.frame_lock: Lock = Lock()
         self.latest_frame: NDArray[Any] | None = None
+        self.latest_timestamp: float | None = None
         self.new_frame_event: Event = Event()
 
     def __str__(self) -> str:
@@ -83,12 +102,17 @@ class ZMQCamera(Camera):
 
     @property
     def is_connected(self) -> bool:
+        """Checks if the ZMQ socket is initialized and connected."""
         return self._connected and self.context is not None and self.socket is not None
 
+    @check_if_already_connected
     def connect(self, warmup: bool = True) -> None:
-        """Connect to ZMQ camera server."""
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
+        """Connect to ZMQ camera server.
+
+        Args:
+            warmup (bool): If True, waits for the camera to provide at least one
+                           valid frame before returning. Defaults to True.
+        """
 
         logger.info(f"Connecting to {self}...")
 
@@ -103,17 +127,28 @@ class ZMQCamera(Camera):
             self.socket.connect(f"tcp://{self.server_address}:{self.port}")
             self._connected = True
 
-            # Auto-detect resolution
+            # Auto-detect resolution if not provided
             if self.width is None or self.height is None:
-                h, w = self.read().shape[:2]
+                # Read directly from hardware because the thread isn't running yet
+                temp_frame = self._read_from_hardware()
+                h, w = temp_frame.shape[:2]
                 self.height = h
                 self.width = w
-                logger.info(f"{self} resolution: {w}x{h}")
+                logger.info(f"{self} resolution detected: {w}x{h}")
 
+            self._start_read_thread()
             logger.info(f"{self} connected.")
 
             if warmup:
-                time.sleep(0.1)
+                # Ensure we have captured at least one frame via the thread
+                start_time = time.time()
+                while time.time() - start_time < (self.config.warmup_s):  # Wait a bit more than timeout
+                    self.async_read(timeout_ms=self.config.warmup_s * 1000)
+                    time.sleep(0.1)
+
+                with self.frame_lock:
+                    if self.latest_frame is None:
+                        raise ConnectionError(f"{self} failed to capture frames during warmup.")
 
         except Exception as e:
             self._cleanup()
@@ -131,15 +166,14 @@ class ZMQCamera(Camera):
 
     @staticmethod
     def find_cameras() -> list[dict[str, Any]]:
-        """ZMQ cameras require manual configuration (server address/port)."""
-        return []
-
-    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
         """
-        Read a single frame from the ZMQ camera.
+        Detection not implemented for ZMQ cameras. These cameras require manual configuration (server address/port).
+        """
+        raise NotImplementedError("Camera detection is not implemented for ZMQ cameras.")
 
-        Returns:
-            np.ndarray: Decoded frame (height, width, 3)
+    def _read_from_hardware(self) -> NDArray[Any]:
+        """
+        Reads a single frame directly from the ZMQ socket.
         """
         if not self.is_connected or self.socket is None:
             raise DeviceNotConnectedError(f"{self} is not connected.")
@@ -147,6 +181,7 @@ class ZMQCamera(Camera):
         try:
             message = self.socket.recv_string()
         except Exception as e:
+            # Check for ZMQ timeout (EAGAIN/Again) without requiring global zmq import
             if type(e).__name__ == "Again":
                 raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e
             raise
@@ -176,42 +211,114 @@ class ZMQCamera(Camera):
 
         return frame
 
+    @check_if_not_connected
+    def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]:
+        """
+        Reads a single frame synchronously from the camera.
+
+        This is a blocking call. It waits for the next available frame from the
+        camera background thread.
+
+        Returns:
+            np.ndarray: Decoded frame (height, width, 3)
+        """
+        start_time = time.perf_counter()
+
+        if color_mode is not None:
+            logger.warning(
+                f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
+            )
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        self.new_frame_event.clear()
+        frame = self.async_read(timeout_ms=10000)
+
+        read_duration_ms = (time.perf_counter() - start_time) * 1e3
+        logger.debug(f"{self} read took: {read_duration_ms:.1f}ms")
+
+        return frame
+
     def _read_loop(self) -> None:
-        while self.stop_event and not self.stop_event.is_set():
+        """
+        Internal loop run by the background thread for asynchronous reading.
+        """
+        if self.stop_event is None:
+            raise RuntimeError(f"{self}: stop_event is not initialized.")
+
+        failure_count = 0
+        while not self.stop_event.is_set():
             try:
-                frame = self.read()
+                frame = self._read_from_hardware()
+                capture_time = time.perf_counter()
+
                 with self.frame_lock:
                     self.latest_frame = frame
+                    self.latest_timestamp = capture_time
                 self.new_frame_event.set()
+                failure_count = 0
+
             except DeviceNotConnectedError:
                 break
-            except TimeoutError:
-                pass
-            except Exception as e:
-                logger.warning(f"Read error: {e}")
+            except (TimeoutError, Exception) as e:
+                if failure_count <= 10:
+                    failure_count += 1
+                    logger.warning(f"Read error: {e}")
+                else:
+                    raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e
 
     def _start_read_thread(self) -> None:
-        if self.thread and self.thread.is_alive():
-            return
+        if self.stop_event is not None:
+            self.stop_event.set()
+        if self.thread is not None and self.thread.is_alive():
+            self.thread.join(timeout=2.0)
+
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
         self.stop_event = Event()
-        self.thread = Thread(target=self._read_loop, daemon=True)
+        self.thread = Thread(target=self._read_loop, daemon=True, name=f"{self}_read_loop")
         self.thread.start()
+        time.sleep(0.1)
 
     def _stop_read_thread(self) -> None:
-        if self.stop_event:
+        if self.stop_event is not None:
             self.stop_event.set()
-        if self.thread and self.thread.is_alive():
+
+        if self.thread is not None and self.thread.is_alive():
             self.thread.join(timeout=2.0)
+
         self.thread = None
         self.stop_event = None
 
-    def async_read(self, timeout_ms: float = 10000) -> NDArray[Any]:
-        """Read latest frame asynchronously (non-blocking)."""
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
 
-        if not self.thread or not self.thread.is_alive():
-            self._start_read_thread()
+    @check_if_not_connected
+    def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
+        """
+        Reads the latest available frame asynchronously.
+
+        Args:
+            timeout_ms (float): Maximum time in milliseconds to wait for a frame
+                to become available. Defaults to 200ms.
+
+        Returns:
+            np.ndarray: The latest captured frame.
+
+        Raises:
+            DeviceNotConnectedError: If the camera is not connected.
+            TimeoutError: If no frame data becomes available within the specified timeout.
+            RuntimeError: If the background thread is not running.
+        """
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
 
         if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
             raise TimeoutError(f"{self} async_read timeout after {timeout_ms}ms")
@@ -225,11 +332,54 @@ class ZMQCamera(Camera):
 
         return frame
 
+    @check_if_not_connected
+    def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
+        """Return the most recent frame captured immediately (Peeking).
+
+        This method is non-blocking and returns whatever is currently in the
+        memory buffer. The frame may be stale,
+        meaning it could have been captured a while ago (hanging camera scenario e.g.).
+
+        Returns:
+            NDArray[Any]: The frame image (numpy array).
+
+        Raises:
+            TimeoutError: If the latest frame is older than `max_age_ms`.
+            DeviceNotConnectedError: If the camera is not connected.
+            RuntimeError: If the camera is connected but has not captured any frames yet.
+        """
+
+        if self.thread is None or not self.thread.is_alive():
+            raise RuntimeError(f"{self} read thread is not running.")
+
+        with self.frame_lock:
+            frame = self.latest_frame
+            timestamp = self.latest_timestamp
+
+        if frame is None or timestamp is None:
+            raise RuntimeError(f"{self} has not captured any frames yet.")
+
+        age_ms = (time.perf_counter() - timestamp) * 1e3
+        if age_ms > max_age_ms:
+            raise TimeoutError(
+                f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)."
+            )
+
+        return frame
+
     def disconnect(self) -> None:
         """Disconnect from ZMQ camera."""
-        if not self.is_connected and not self.thread:
+        if not self.is_connected and self.thread is None:
             raise DeviceNotConnectedError(f"{self} not connected.")
 
-        self._stop_read_thread()
+        if self.thread is not None:
+            self._stop_read_thread()
+
         self._cleanup()
+
+        with self.frame_lock:
+            self.latest_frame = None
+            self.latest_timestamp = None
+            self.new_frame_event.clear()
+
         logger.info(f"{self} disconnected.")

src/lerobot/cameras/zmq/configuration_zmq.py

@@ -29,12 +29,10 @@ class ZMQCameraConfig(CameraConfig):
     camera_name: str = "zmq_camera"
     color_mode: ColorMode = ColorMode.RGB
     timeout_ms: int = 5000
+    warmup_s: int = 1
 
     def __post_init__(self) -> None:
-        if self.color_mode not in (ColorMode.RGB, ColorMode.BGR):
-            raise ValueError(
-                f"`color_mode` is expected to be {ColorMode.RGB.value} or {ColorMode.BGR.value}, but {self.color_mode} is provided."
-            )
+        self.color_mode = ColorMode(self.color_mode)
 
         if self.timeout_ms <= 0:
             raise ValueError(f"`timeout_ms` must be positive, but {self.timeout_ms} is provided.")

src/lerobot/configs/policies.py

@@ -45,12 +45,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
-        input_shapes: A dictionary defining the shapes of the input data for the policy.
-        output_shapes: A dictionary defining the shapes of the output data for the policy.
-        input_normalization_modes: A dictionary with key representing the modality and the value specifies the
-            normalization mode to apply.
-        output_normalization_modes: Similar dictionary as `input_normalization_modes`, but to unnormalize to
-            the original scale.
+        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
+            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
+            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
+            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
     """
 
     n_obs_steps: int = 1

src/lerobot/datasets/aggregate.py

@@ -116,6 +116,9 @@ def update_meta_data(
     Adjusts all indices and timestamps to account for previously aggregated
     data and videos in the destination dataset.
 
+    For data file indices, uses the 'src_to_dst' mapping from aggregate_data()
+    to correctly map source file indices to their destination locations.
+
     Args:
         df: DataFrame containing the metadata to be updated.
         dst_meta: Destination dataset metadata.
@@ -129,8 +132,50 @@ def update_meta_data(
 
     df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"]
     df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"]
-    df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
-    df["data/file_index"] = df["data/file_index"] + data_idx["file"]
+
+    # Update data file indices using source-to-destination mapping
+    # This is critical for handling datasets that are already results of a merge
+    data_src_to_dst = data_idx.get("src_to_dst", {})
+    if data_src_to_dst:
+        # Store original indices for lookup
+        df["_orig_data_chunk"] = df["data/chunk_index"].copy()
+        df["_orig_data_file"] = df["data/file_index"].copy()
+
+        # Vectorized mapping from (src_chunk, src_file) to (dst_chunk, dst_file)
+        # This is much faster than per-row iteration for large metadata tables
+        mapping_index = pd.MultiIndex.from_tuples(
+            list(data_src_to_dst.keys()),
+            names=["chunk_index", "file_index"],
+        )
+        mapping_values = list(data_src_to_dst.values())
+        mapping_df = pd.DataFrame(
+            mapping_values,
+            index=mapping_index,
+            columns=["dst_chunk", "dst_file"],
+        )
+
+        # Construct a MultiIndex for each row based on original data indices
+        row_index = pd.MultiIndex.from_arrays(
+            [df["_orig_data_chunk"], df["_orig_data_file"]],
+            names=["chunk_index", "file_index"],
+        )
+
+        # Align mapping to rows; missing keys fall back to the default destination
+        reindexed = mapping_df.reindex(row_index)
+        reindexed[["dst_chunk", "dst_file"]] = reindexed[["dst_chunk", "dst_file"]].fillna(
+            {"dst_chunk": data_idx["chunk"], "dst_file": data_idx["file"]}
+        )
+
+        # Assign mapped destination indices back to the DataFrame
+        df["data/chunk_index"] = reindexed["dst_chunk"].to_numpy()
+        df["data/file_index"] = reindexed["dst_file"].to_numpy()
+
+        # Clean up temporary columns
+        df = df.drop(columns=["_orig_data_chunk", "_orig_data_file"])
+    else:
+        # Fallback to simple offset (backward compatibility for single-file sources)
+        df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"]
+        df["data/file_index"] = df["data/file_index"] + data_idx["file"]
     for key, video_idx in videos_idx.items():
         # Store original video file indices before updating
         orig_chunk_col = f"videos/{key}/chunk_index"
@@ -146,8 +191,7 @@ def update_meta_data(
         if src_to_dst:
             # Map each episode to its correct destination file and apply offset
             for idx in df.index:
-                # Convert to Python int to avoid numpy type mismatch in dict lookup
-                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
+                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
 
                 # Get destination chunk/file for this source file
                 dst_chunk, dst_file = src_to_dst.get(src_key, (video_idx["chunk"], video_idx["file"]))
@@ -163,8 +207,7 @@ def update_meta_data(
             df[orig_chunk_col] = video_idx["chunk"]
             df[orig_file_col] = video_idx["file"]
             for idx in df.index:
-                # Convert to Python int to avoid numpy type mismatch in dict lookup
-                src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"]))
+                src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"])
                 offset = src_to_offset.get(src_key, 0)
                 df.at[idx, f"videos/{key}/from_timestamp"] += offset
                 df.at[idx, f"videos/{key}/to_timestamp"] += offset
@@ -262,6 +305,10 @@ def aggregate_datasets(
 
         meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx)
 
+        # Clear the src_to_dst mapping after processing each source dataset
+        # to avoid interference between different source datasets
+        data_idx.pop("src_to_dst", None)
+
         dst_meta.info["total_episodes"] += src_meta.total_episodes
         dst_meta.info["total_frames"] += src_meta.total_frames
 
@@ -312,10 +359,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu
         dst_file_durations = video_idx["dst_file_durations"]
 
         for src_chunk_idx, src_file_idx in unique_chunk_file_pairs:
-            # Convert to Python int to ensure consistent dict keys
-            src_chunk_idx = int(src_chunk_idx)
-            src_file_idx = int(src_file_idx)
-
             src_path = src_meta.root / DEFAULT_VIDEO_PATH.format(
                 video_key=key,
                 chunk_index=src_chunk_idx,
@@ -388,10 +431,16 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     Reads source data files, updates indices to match the aggregated dataset,
     and writes them to the destination with proper file rotation.
 
+    Tracks a `src_to_dst` mapping from source (chunk, file) to destination (chunk, file)
+    which is critical for correctly updating episode metadata when source datasets
+    have multiple data files (e.g., from a previous merge operation).
+
     Args:
         src_meta: Source dataset metadata.
         dst_meta: Destination dataset metadata.
         data_idx: Dictionary tracking data chunk and file indices.
+        data_files_size_in_mb: Maximum size for data files in MB.
+        chunk_size: Maximum number of files per chunk.
 
     Returns:
         dict: Updated data_idx with current chunk and file indices.
@@ -409,6 +458,10 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
     # retrieve features schema for proper image typing in parquet
     hf_features = get_hf_features_from_features(dst_meta.features) if contains_images else None
 
+    # Track source to destination file mapping for metadata update
+    # This is critical for handling datasets that are already results of a merge
+    src_to_dst: dict[tuple[int, int], tuple[int, int]] = {}
+
     for src_chunk_idx, src_file_idx in unique_chunk_file_ids:
         src_path = src_meta.root / DEFAULT_DATA_PATH.format(
             chunk_index=src_chunk_idx, file_index=src_file_idx
@@ -421,7 +474,9 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
             df = pd.read_parquet(src_path)
         df = update_data_df(df, src_meta, dst_meta)
 
-        data_idx = append_or_create_parquet_file(
+        # Write data and get the actual destination file it was written to
+        # This avoids duplicating the rotation logic here
+        data_idx, (dst_chunk, dst_file) = append_or_create_parquet_file(
             df,
             src_path,
             data_idx,
@@ -433,6 +488,12 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si
             hf_features=hf_features,
         )
 
+        # Record the mapping from source to actual destination
+        src_to_dst[(src_chunk_idx, src_file_idx)] = (dst_chunk, dst_file)
+
+    # Add the mapping to data_idx for use in metadata update
+    data_idx["src_to_dst"] = src_to_dst
+
     return data_idx
 
 
@@ -473,7 +534,7 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx):
             videos_idx,
         )
 
-        meta_idx = append_or_create_parquet_file(
+        meta_idx, _ = append_or_create_parquet_file(
             df,
             src_path,
             meta_idx,
@@ -501,7 +562,7 @@ def append_or_create_parquet_file(
     contains_images: bool = False,
     aggr_root: Path = None,
     hf_features: datasets.Features | None = None,
-):
+) -> tuple[dict[str, int], tuple[int, int]]:
     """Appends data to an existing parquet file or creates a new one based on size constraints.
 
     Manages file rotation when size limits are exceeded to prevent individual files
@@ -519,9 +580,11 @@ def append_or_create_parquet_file(
         hf_features: Optional HuggingFace Features schema for proper image typing.
 
     Returns:
-        dict: Updated index dictionary with current chunk and file indices.
+        tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict
+               and (dst_chunk, dst_file) is the actual destination file the data was written to.
     """
-    dst_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"])
+    dst_chunk, dst_file = idx["chunk"], idx["file"]
+    dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
 
     if not dst_path.exists():
         dst_path.parent.mkdir(parents=True, exist_ok=True)
@@ -529,14 +592,15 @@ def append_or_create_parquet_file(
             to_parquet_with_hf_images(df, dst_path, features=hf_features)
         else:
             df.to_parquet(dst_path)
-        return idx
+        return idx, (dst_chunk, dst_file)
 
     src_size = get_parquet_file_size_in_mb(src_path)
     dst_size = get_parquet_file_size_in_mb(dst_path)
 
     if dst_size + src_size >= max_mb:
         idx["chunk"], idx["file"] = update_chunk_file_indices(idx["chunk"], idx["file"], chunk_size)
-        new_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"])
+        dst_chunk, dst_file = idx["chunk"], idx["file"]
+        new_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file)
         new_path.parent.mkdir(parents=True, exist_ok=True)
         final_df = df
         target_path = new_path
@@ -555,7 +619,7 @@ def append_or_create_parquet_file(
     else:
         final_df.to_parquet(target_path)
 
-    return idx
+    return idx, (dst_chunk, dst_file)
 
 
 def finalize_aggregation(aggr_meta, all_metadata):

src/lerobot/datasets/dataset_tools.py

@@ -1396,6 +1396,132 @@ BYTES_PER_KIB = 1024
 BYTES_PER_MIB = BYTES_PER_KIB * BYTES_PER_KIB
 
 
+def modify_tasks(
+    dataset: LeRobotDataset,
+    new_task: str | None = None,
+    episode_tasks: dict[int, str] | None = None,
+) -> LeRobotDataset:
+    """Modify tasks in a LeRobotDataset.
+
+    This function allows you to either:
+    1. Set a single task for the entire dataset (using `new_task`)
+    2. Set specific tasks for specific episodes (using `episode_tasks`)
+
+    You can combine both: `new_task` sets the default, and `episode_tasks` overrides
+    specific episodes.
+
+    The dataset is modified in-place, updating only the task-related files:
+    - meta/tasks.parquet
+    - data/**/*.parquet (task_index column)
+    - meta/episodes/**/*.parquet (tasks column)
+    - meta/info.json (total_tasks)
+
+    Args:
+        dataset: The source LeRobotDataset to modify.
+        new_task: A single task string to apply to all episodes. If None and episode_tasks
+            is also None, raises an error.
+        episode_tasks: Optional dict mapping episode indices to their task strings.
+            Overrides `new_task` for specific episodes.
+
+
+    Examples:
+        Set a single task for all episodes:
+            dataset = modify_tasks(dataset, new_task="Pick up the cube")
+
+        Set different tasks for specific episodes:
+            dataset = modify_tasks(
+                dataset,
+                episode_tasks={0: "Task A", 1: "Task B", 2: "Task A"}
+            )
+
+        Set a default task with overrides:
+            dataset = modify_tasks(
+                dataset,
+                new_task="Default task",
+                episode_tasks={5: "Special task for episode 5"}
+            )
+    """
+    if new_task is None and episode_tasks is None:
+        raise ValueError("Must specify at least one of new_task or episode_tasks")
+
+    if episode_tasks is not None:
+        valid_indices = set(range(dataset.meta.total_episodes))
+        invalid = set(episode_tasks.keys()) - valid_indices
+        if invalid:
+            raise ValueError(f"Invalid episode indices: {invalid}")
+
+    # Ensure episodes metadata is loaded
+    if dataset.meta.episodes is None:
+        dataset.meta.episodes = load_episodes(dataset.root)
+
+    # Build the mapping from episode index to task string
+    episode_to_task: dict[int, str] = {}
+    for ep_idx in range(dataset.meta.total_episodes):
+        if episode_tasks and ep_idx in episode_tasks:
+            episode_to_task[ep_idx] = episode_tasks[ep_idx]
+        elif new_task is not None:
+            episode_to_task[ep_idx] = new_task
+        else:
+            # Keep original task if not overridden and no default provided
+            original_tasks = dataset.meta.episodes[ep_idx]["tasks"]
+            if not original_tasks:
+                raise ValueError(f"Episode {ep_idx} has no tasks and no default task was provided")
+            episode_to_task[ep_idx] = original_tasks[0]
+
+    # Collect all unique tasks and create new task mapping
+    unique_tasks = sorted(set(episode_to_task.values()))
+    new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks)
+    task_to_index = {task: idx for idx, task in enumerate(unique_tasks)}
+
+    logging.info(f"Modifying tasks in {dataset.repo_id}")
+    logging.info(f"New tasks: {unique_tasks}")
+
+    root = dataset.root
+
+    # Update data files - modify task_index column
+    logging.info("Updating data files...")
+    data_dir = root / DATA_DIR
+
+    for parquet_path in tqdm(sorted(data_dir.rglob("*.parquet")), desc="Updating data"):
+        df = pd.read_parquet(parquet_path)
+
+        # Build a mapping from episode_index to new task_index for rows in this file
+        episode_indices_in_file = df["episode_index"].unique()
+        ep_to_new_task_idx = {
+            ep_idx: task_to_index[episode_to_task[ep_idx]] for ep_idx in episode_indices_in_file
+        }
+
+        # Update task_index column
+        df["task_index"] = df["episode_index"].map(ep_to_new_task_idx)
+        df.to_parquet(parquet_path, index=False)
+
+    # Update episodes metadata - modify tasks column
+    logging.info("Updating episodes metadata...")
+    episodes_dir = root / "meta" / "episodes"
+
+    for parquet_path in tqdm(sorted(episodes_dir.rglob("*.parquet")), desc="Updating episodes"):
+        df = pd.read_parquet(parquet_path)
+
+        # Update tasks column
+        df["tasks"] = df["episode_index"].apply(lambda ep_idx: [episode_to_task[ep_idx]])
+        df.to_parquet(parquet_path, index=False)
+
+    # Write new tasks.parquet
+    write_tasks(new_task_df, root)
+
+    # Update info.json
+    dataset.meta.info["total_tasks"] = len(unique_tasks)
+    write_info(dataset.meta.info, root)
+
+    # Reload metadata to reflect changes
+    dataset.meta.tasks = new_task_df
+    dataset.meta.episodes = load_episodes(root)
+
+    logging.info(f"Tasks: {unique_tasks}")
+
+    return dataset
+
+
 def convert_image_to_video_dataset(
     dataset: LeRobotDataset,
     output_dir: Path,

src/lerobot/datasets/lerobot_dataset.py

@@ -57,6 +57,7 @@ from lerobot.datasets.utils import (
     load_info,
     load_nested_dataset,
     load_stats,
+    load_subtasks,
     load_tasks,
     update_chunk_file_indices,
     validate_episode_buffer,
@@ -162,6 +163,7 @@ class LeRobotDatasetMetadata:
         self.info = load_info(self.root)
         check_version_compatibility(self.repo_id, self._version, CODEBASE_VERSION)
         self.tasks = load_tasks(self.root)
+        self.subtasks = load_subtasks(self.root)
         self.episodes = load_episodes(self.root)
         self.stats = load_stats(self.root)
 
@@ -518,6 +520,7 @@ class LeRobotDatasetMetadata:
         _validate_feature_names(features)
 
         obj.tasks = None
+        obj.subtasks = None
         obj.episodes = None
         obj.stats = None
         obj.info = create_empty_dataset_info(
@@ -1075,6 +1078,12 @@ class LeRobotDataset(torch.utils.data.Dataset):
         # Add task as a string
         task_idx = item["task_index"].item()
         item["task"] = self.meta.tasks.iloc[task_idx].name
+
+        # add subtask information if available
+        if "subtask_index" in self.features and self.meta.subtasks is not None:
+            subtask_idx = item["subtask_index"].item()
+            item["subtask"] = self.meta.subtasks.iloc[subtask_idx].name
+
         return item
 
     def __repr__(self):

src/lerobot/datasets/transforms.py

@@ -216,16 +216,17 @@ class ImageTransformsConfig:
 
 
 def make_transform_from_config(cfg: ImageTransformConfig):
-    if cfg.type == "Identity":
-        return v2.Identity(**cfg.kwargs)
-    elif cfg.type == "ColorJitter":
-        return v2.ColorJitter(**cfg.kwargs)
-    elif cfg.type == "SharpnessJitter":
+    if cfg.type == "SharpnessJitter":
         return SharpnessJitter(**cfg.kwargs)
-    elif cfg.type == "RandomAffine":
-        return v2.RandomAffine(**cfg.kwargs)
-    else:
-        raise ValueError(f"Transform '{cfg.type}' is not valid.")
+
+    transform_cls = getattr(v2, cfg.type, None)
+    if isinstance(transform_cls, type) and issubclass(transform_cls, Transform):
+        return transform_cls(**cfg.kwargs)
+
+    raise ValueError(
+        f"Transform '{cfg.type}' is not valid. It must be a class in "
+        f"torchvision.transforms.v2 or 'SharpnessJitter'."
+    )
 
 
 class ImageTransforms(Transform):

src/lerobot/datasets/utils.py

@@ -60,6 +60,7 @@ VIDEO_DIR = "videos"
 
 CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}"
 DEFAULT_TASKS_PATH = "meta/tasks.parquet"
+DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet"
 DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_DATA_PATH = DATA_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet"
 DEFAULT_VIDEO_PATH = VIDEO_DIR + "/{video_key}/" + CHUNK_FILE_PATTERN + ".mp4"
@@ -353,6 +354,14 @@ def load_tasks(local_dir: Path) -> pandas.DataFrame:
     return tasks
 
 
+def load_subtasks(local_dir: Path) -> pandas.DataFrame | None:
+    """Load subtasks from subtasks.parquet if it exists."""
+    subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH
+    if subtasks_path.exists():
+        return pd.read_parquet(subtasks_path)
+    return None
+
+
 def write_episodes(episodes: Dataset, local_dir: Path) -> None:
     """Write episode metadata to a parquet file in the LeRobot v3.0 format.
     This function writes episode-level metadata to a single parquet file.

src/lerobot/envs/configs.py

@@ -205,6 +205,7 @@ class ObservationConfig:
 
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
+    add_ee_pose_to_observation: bool = False
     display_cameras: bool = False
 
 

src/lerobot/envs/libero.py

@@ -112,6 +112,7 @@ class LiberoEnv(gym.Env):
         visualization_height: int = 480,
         init_states: bool = True,
         episode_index: int = 0,
+        n_envs: int = 1,
         camera_name_mapping: dict[str, str] | None = None,
         num_steps_wait: int = 10,
         control_mode: str = "relative",
@@ -145,7 +146,9 @@ class LiberoEnv(gym.Env):
         self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
-        self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
+        self._reset_stride = n_envs  # when performing a reset, append `_reset_stride` to `init_state_id`.
+
+        self.init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
@@ -295,7 +298,8 @@ class LiberoEnv(gym.Env):
         self._env.seed(seed)
         raw_obs = self._env.reset()
         if self.init_states and self._init_states is not None:
-            raw_obs = self._env.set_init_state(self._init_states[self._init_state_id])
+            raw_obs = self._env.set_init_state(self._init_states[self.init_state_id % len(self._init_states)])
+            self.init_state_id += self._reset_stride  # Change init_state_id when reset
 
         # After reset, objects may be unstable (slightly floating, intersecting, etc.).
         # Step the simulator with a no-op action for a few frames so everything settles.
@@ -373,6 +377,7 @@ def _make_env_fns(
             init_states=init_states,
             episode_length=episode_length,
             episode_index=episode_index,
+            n_envs=n_envs,
             control_mode=control_mode,
             **local_kwargs,
         )

src/lerobot/motors/calibration_gui.py

@@ -221,7 +221,7 @@ class RangeFinderGUI:
 
         self.bus = bus
         self.groups = groups if groups is not None else {"all": list(bus.motors)}
-        self.group_names = list(groups)
+        self.group_names = list(self.groups)
         self.current_group = self.group_names[0]
 
         if not bus.is_connected:
@@ -230,18 +230,20 @@ class RangeFinderGUI:
         self.calibration = bus.read_calibration()
         self.res_table = bus.model_resolution_table
         self.present_cache = {
-            m: bus.read("Present_Position", m, normalize=False) for motors in groups.values() for m in motors
+            m: bus.read("Present_Position", m, normalize=False)
+            for motors in self.groups.values()
+            for m in motors
         }
 
         pygame.init()
         self.font = pygame.font.Font(None, FONT_SIZE)
 
-        label_pad = max(self.font.size(m)[0] for ms in groups.values() for m in ms)
+        label_pad = max(self.font.size(m)[0] for ms in self.groups.values() for m in ms)
         self.label_pad = label_pad
         width = 40 + label_pad + BAR_LEN + 6 + BTN_W + 10 + SAVE_W + 10
         self.controls_bottom = 10 + SAVE_H
         self.base_y = self.controls_bottom + TOP_GAP
-        height = self.base_y + PADDING_Y * len(groups[self.current_group]) + 40
+        height = self.base_y + PADDING_Y * len(self.groups[self.current_group]) + 40
 
         self.screen = pygame.display.set_mode((width, height))
         pygame.display.set_caption("Motors range finder")

src/lerobot/motors/damiao/damiao.py

@@ -23,17 +23,20 @@ from copy import deepcopy
 from functools import cached_property
 from typing import TYPE_CHECKING, Any, TypedDict
 
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.import_utils import _can_available
 
 if TYPE_CHECKING or _can_available:
     import can
 else:
-    can.Message = object
-    can.interface = None
+
+    class can:  # noqa: N801
+        Message = object
+        interface = None
+
 
 import numpy as np
 
-from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 from lerobot.utils.robot_utils import precise_sleep
 from lerobot.utils.utils import enter_pressed, move_cursor_up
 
@@ -152,6 +155,7 @@ class DamiaoMotorsBus(MotorsBusBase):
         """Check if the CAN bus is connected."""
         return self._is_connected and self.canbus is not None
 
+    @check_if_already_connected
     def connect(self, handshake: bool = True) -> None:
         """
         Open the CAN bus and initialize communication.
@@ -159,10 +163,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         Args:
             handshake: If True, ping all motors to verify they're present
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(
-                f"{self.__class__.__name__}('{self.port}') is already connected."
-            )
 
         try:
             # Auto-detect interface type based on port name
@@ -206,11 +206,34 @@ class DamiaoMotorsBus(MotorsBusBase):
         Raises ConnectionError if any motor fails to respond.
         """
         logger.info("Starting handshake with motors...")
-        missing_motors = []
 
+        # Drain any pending messages
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
+        while self.canbus.recv(timeout=0.01):
+            pass
+
+        missing_motors = []
         for motor_name in self.motors:
-            msg = self._refresh_motor(motor_name)
-            if msg is None:
+            motor_id = self._get_motor_id(motor_name)
+            recv_id = self._get_motor_recv_id(motor_name)
+
+            # Send enable command
+            data = [0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, CAN_CMD_ENABLE]
+            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
+            self.canbus.send(msg)
+
+            # Wait for response with longer timeout
+            response = None
+            start_time = time.time()
+            while time.time() - start_time < 0.1:
+                response = self.canbus.recv(timeout=0.1)
+                if response and response.arbitration_id == recv_id:
+                    break
+                response = None
+
+            if response is None:
                 missing_motors.append(motor_name)
             else:
                 self._process_response(motor_name, msg)
@@ -223,6 +246,7 @@ class DamiaoMotorsBus(MotorsBusBase):
             )
         logger.info("Handshake successful. All motors ready.")
 
+    @check_if_not_connected
     def disconnect(self, disable_torque: bool = True) -> None:
         """
         Close the CAN bus connection.
@@ -230,8 +254,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         Args:
             disable_torque: If True, disable torque on all motors before disconnecting
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self.__class__.__name__}('{self.port}') is not connected.")
 
         if disable_torque:
             try:
@@ -259,7 +281,11 @@ class DamiaoMotorsBus(MotorsBusBase):
         motor_name = self._get_motor_name(motor)
         recv_id = self._get_motor_recv_id(motor)
         data = [0xFF] * 7 + [command_byte]
-        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
+
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         self.canbus.send(msg)
         if msg := self._recv_motor_response(expected_recv_id=recv_id):
             self._process_response(motor_name, msg)
@@ -317,7 +343,11 @@ class DamiaoMotorsBus(MotorsBusBase):
         motor_id = self._get_motor_id(motor)
         recv_id = self._get_motor_recv_id(motor)
         data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
-        msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False)
+        msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False, is_fd=self.use_can_fd)
+
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         self.canbus.send(msg)
         return self._recv_motor_response(expected_recv_id=recv_id)
 
@@ -333,6 +363,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         Returns:
             CAN message if received, None otherwise
         """
+
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         try:
             start_time = time.time()
             messages_seen = []
@@ -371,10 +405,13 @@ class DamiaoMotorsBus(MotorsBusBase):
         Returns:
             Dictionary mapping recv_id to CAN message
         """
-        responses = {}
+        responses: dict[int, can.Message] = {}
         expected_set = set(expected_recv_ids)
         start_time = time.time()
 
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         try:
             while len(responses) < len(expected_recv_ids) and (time.time() - start_time) < timeout:
                 # 100us poll timeout
@@ -438,8 +475,11 @@ class DamiaoMotorsBus(MotorsBusBase):
         motor_name = self._get_motor_name(motor)
         motor_type = self._motor_types[motor_name]
 
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         data = self._encode_mit_packet(motor_type, kp, kd, position_degrees, velocity_deg_per_sec, torque)
-        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+        msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
         self.canbus.send(msg)
 
         recv_id = self._get_motor_recv_id(motor)
@@ -465,6 +505,9 @@ class DamiaoMotorsBus(MotorsBusBase):
 
         recv_id_to_motor: dict[int, str] = {}
 
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         # Step 1: Send all MIT control commands
         for motor, (kp, kd, position_degrees, velocity_deg_per_sec, torque) in commands.items():
             motor_id = self._get_motor_id(motor)
@@ -472,7 +515,7 @@ class DamiaoMotorsBus(MotorsBusBase):
             motor_type = self._motor_types[motor_name]
 
             data = self._encode_mit_packet(motor_type, kp, kd, position_degrees, velocity_deg_per_sec, torque)
-            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+            msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd)
             self.canbus.send(msg)
 
             recv_id_to_motor[self._get_motor_recv_id(motor)] = motor_name
@@ -539,10 +582,9 @@ class DamiaoMotorsBus(MotorsBusBase):
         except Exception as e:
             logger.warning(f"Failed to decode response from {motor}: {e}")
 
+    @check_if_not_connected
     def read(self, data_name: str, motor: str) -> Value:
         """Read a value from a single motor. Positions are always in degrees."""
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Refresh motor to get latest state
         msg = self._refresh_motor(motor)
@@ -572,6 +614,7 @@ class DamiaoMotorsBus(MotorsBusBase):
             raise ValueError(f"Unknown data_name: {data_name}")
         return mapping[data_name]
 
+    @check_if_not_connected
     def write(
         self,
         data_name: str,
@@ -582,8 +625,6 @@ class DamiaoMotorsBus(MotorsBusBase):
         Write a value to a single motor. Positions are always in degrees.
         Can write 'Goal_Position', 'Kp', or 'Kd'.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if data_name in ("Kp", "Kd"):
             self._gains[motor][data_name.lower()] = float(value)
@@ -633,14 +674,18 @@ class DamiaoMotorsBus(MotorsBusBase):
 
     def _batch_refresh(self, motors: list[str]) -> None:
         """Internal helper to refresh a list of motors and update cache."""
+
+        if self.canbus is None:
+            raise RuntimeError("CAN bus is not initialized.")
+
         # Send refresh commands
         for motor in motors:
             motor_id = self._get_motor_id(motor)
             data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0]
-            msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False)
+            msg = can.Message(
+                arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False, is_fd=self.use_can_fd
+            )
             self.canbus.send(msg)
-            # Small delay to reduce bus congestion if necessary, though removed in sync_read previously
-            # precise_sleep(PRECISE_SLEEP_SEC)
 
         # Collect responses
         expected_recv_ids = [self._get_motor_recv_id(m) for m in motors]
@@ -655,10 +700,12 @@ class DamiaoMotorsBus(MotorsBusBase):
             else:
                 logger.warning(f"Packet drop: {motor} (ID: 0x{recv_id:02X}). Using last known state.")
 
-    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
+    @check_if_not_connected
+    def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
         """
         Write values to multiple motors simultaneously. Positions are always in degrees.
         """
+
         if data_name in ("Kp", "Kd"):
             key = data_name.lower()
             for motor, val in values.items():
@@ -667,6 +714,8 @@ class DamiaoMotorsBus(MotorsBusBase):
         elif data_name == "Goal_Position":
             # Step 1: Send all MIT control commands
             recv_id_to_motor: dict[int, str] = {}
+            if self.canbus is None:
+                raise RuntimeError("CAN bus is not initialized.")
             for motor, value_degrees in values.items():
                 motor_id = self._get_motor_id(motor)
                 motor_name = self._get_motor_name(motor)
@@ -676,7 +725,9 @@ class DamiaoMotorsBus(MotorsBusBase):
                 kd = self._gains[motor]["kd"]
 
                 data = self._encode_mit_packet(motor_type, kp, kd, float(value_degrees), 0.0, 0.0)
-                msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False)
+                msg = can.Message(
+                    arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd
+                )
                 self.canbus.send(msg)
                 precise_sleep(PRECISE_TIMEOUT_SEC)
 
@@ -707,9 +758,9 @@ class DamiaoMotorsBus(MotorsBusBase):
 
     def record_ranges_of_motion(
         self,
-        motors: NameOrID | list[NameOrID] | None = None,
+        motors: str | list[str] | None = None,
         display_values: bool = True,
-    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
+    ) -> tuple[dict[str, Value], dict[str, Value]]:
         """
         Interactively record the min/max values of each motor in degrees.
 

src/lerobot/motors/dynamixel/dynamixel.py

@@ -181,10 +181,10 @@ class DynamixelMotorsBus(SerialMotorsBus):
         for motor, m in self.motors.items():
             calibration[motor] = MotorCalibration(
                 id=m.id,
-                drive_mode=drive_modes[motor],
-                homing_offset=offsets[motor],
-                range_min=mins[motor],
-                range_max=maxes[motor],
+                drive_mode=int(drive_modes[motor]),
+                homing_offset=int(offsets[motor]),
+                range_min=int(mins[motor]),
+                range_max=int(maxes[motor]),
             )
 
         return calibration
@@ -198,7 +198,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         if cache:
             self.calibration = calibration_dict
 
-    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
 
@@ -206,7 +206,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         addr, length = get_address(self.model_ctrl_table, model, "Torque_Enable")
         self._write(addr, length, motor, TorqueMode.DISABLED.value, num_retry=num_retry)
 
-    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
 
@@ -235,7 +235,7 @@ class DynamixelMotorsBus(SerialMotorsBus):
         On Dynamixel Motors:
         Present_Position = Actual_Position + Homing_Offset
         """
-        half_turn_homings = {}
+        half_turn_homings: dict[NameOrID, Value] = {}
         for motor, pos in positions.items():
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
@@ -258,6 +258,6 @@ class DynamixelMotorsBus(SerialMotorsBus):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
 
-            return
+            return None
 
         return {id_: data[0] for id_, data in data_list.items()}

src/lerobot/motors/feetech/feetech.py

@@ -126,7 +126,7 @@ class FeetechMotorsBus(SerialMotorsBus):
 
         self.port_handler = scs.PortHandler(self.port)
         # HACK: monkeypatch
-        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(
+        self.port_handler.setPacketTimeout = patch_setPacketTimeout.__get__(  # type: ignore[method-assign]
             self.port_handler, scs.PortHandler
         )
         self.packet_handler = scs.PacketHandler(protocol_version)
@@ -262,9 +262,9 @@ class FeetechMotorsBus(SerialMotorsBus):
             calibration[motor] = MotorCalibration(
                 id=m.id,
                 drive_mode=0,
-                homing_offset=offsets[motor],
-                range_min=mins[motor],
-                range_max=maxes[motor],
+                homing_offset=int(offsets[motor]),
+                range_min=int(mins[motor]),
+                range_max=int(maxes[motor]),
             )
 
         return calibration
@@ -284,7 +284,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         On Feetech Motors:
         Present_Position = Actual_Position - Homing_Offset
         """
-        half_turn_homings = {}
+        half_turn_homings: dict[NameOrID, Value] = {}
         for motor, pos in positions.items():
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
@@ -292,7 +292,7 @@ class FeetechMotorsBus(SerialMotorsBus):
 
         return half_turn_homings
 
-    def disable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def disable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.DISABLED.value, num_retry=num_retry)
             self.write("Lock", motor, 0, num_retry=num_retry)
@@ -303,7 +303,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         addr, length = get_address(self.model_ctrl_table, model, "Lock")
         self._write(addr, length, motor, 0, num_retry=num_retry)
 
-    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         for motor in self._get_motors_list(motors):
             self.write("Torque_Enable", motor, TorqueMode.ENABLED.value, num_retry=num_retry)
             self.write("Lock", motor, 1, num_retry=num_retry)
@@ -334,7 +334,7 @@ class FeetechMotorsBus(SerialMotorsBus):
     def _broadcast_ping(self) -> tuple[dict[int, int], int]:
         import scservo_sdk as scs
 
-        data_list = {}
+        data_list: dict[int, int] = {}
 
         status_length = 6
 
@@ -414,7 +414,7 @@ class FeetechMotorsBus(SerialMotorsBus):
         if not self._is_comm_success(comm):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
-            return
+            return None
 
         ids_errors = {id_: status for id_, status in ids_status.items() if self._is_error(status)}
         if ids_errors:

src/lerobot/motors/motors_bus.py

@@ -23,6 +23,7 @@ from __future__ import annotations
 
 import abc
 import logging
+from collections.abc import Sequence
 from contextlib import contextmanager
 from dataclasses import dataclass
 from enum import Enum
@@ -93,7 +94,7 @@ class MotorsBusBase(abc.ABC):
         pass
 
     @abc.abstractmethod
-    def sync_write(self, data_name: str, values: Value | dict[str, Value]) -> None:
+    def sync_write(self, data_name: str, values: dict[str, Value]) -> None:
         """Write values to multiple motors."""
         pass
 
@@ -179,15 +180,16 @@ class Motor:
 
 
 class PortHandler(Protocol):
-    def __init__(self, port_name):
-        self.is_open: bool
-        self.baudrate: int
-        self.packet_start_time: float
-        self.packet_timeout: float
-        self.tx_time_per_byte: float
-        self.is_using: bool
-        self.port_name: str
-        self.ser: serial.Serial
+    is_open: bool
+    baudrate: int
+    packet_start_time: float
+    packet_timeout: float
+    tx_time_per_byte: float
+    is_using: bool
+    port_name: str
+    ser: serial.Serial
+
+    def __init__(self, port_name: str) -> None: ...
 
     def openPort(self): ...
     def closePort(self): ...
@@ -240,19 +242,22 @@ class PacketHandler(Protocol):
     def regWriteTxRx(self, port, id, address, length, data): ...
     def syncReadTx(self, port, start_address, data_length, param, param_length): ...
     def syncWriteTxOnly(self, port, start_address, data_length, param, param_length): ...
+    def broadcastPing(self, port): ...
 
 
 class GroupSyncRead(Protocol):
-    def __init__(self, port, ph, start_address, data_length):
-        self.port: str
-        self.ph: PortHandler
-        self.start_address: int
-        self.data_length: int
-        self.last_result: bool
-        self.is_param_changed: bool
-        self.param: list
-        self.data_dict: dict
+    port: str
+    ph: PortHandler
+    start_address: int
+    data_length: int
+    last_result: bool
+    is_param_changed: bool
+    param: list
+    data_dict: dict
 
+    def __init__(
+        self, port: PortHandler, ph: PacketHandler, start_address: int, data_length: int
+    ) -> None: ...
     def makeParam(self): ...
     def addParam(self, id): ...
     def removeParam(self, id): ...
@@ -265,15 +270,17 @@ class GroupSyncRead(Protocol):
 
 
 class GroupSyncWrite(Protocol):
-    def __init__(self, port, ph, start_address, data_length):
-        self.port: str
-        self.ph: PortHandler
-        self.start_address: int
-        self.data_length: int
-        self.is_param_changed: bool
-        self.param: list
-        self.data_dict: dict
+    port: str
+    ph: PortHandler
+    start_address: int
+    data_length: int
+    is_param_changed: bool
+    param: list
+    data_dict: dict
 
+    def __init__(
+        self, port: PortHandler, ph: PacketHandler, start_address: int, data_length: int
+    ) -> None: ...
     def makeParam(self): ...
     def addParam(self, id, data): ...
     def removeParam(self, id): ...
@@ -400,7 +407,7 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motor_model(self, motor: NameOrID) -> int:
+    def _get_motor_model(self, motor: NameOrID) -> str:
         if isinstance(motor, str):
             return self.motors[motor].model
         elif isinstance(motor, int):
@@ -408,17 +415,19 @@ class SerialMotorsBus(MotorsBusBase):
         else:
             raise TypeError(f"'{motor}' should be int, str.")
 
-    def _get_motors_list(self, motors: str | list[str] | None) -> list[str]:
+    def _get_motors_list(self, motors: NameOrID | Sequence[NameOrID] | None) -> list[str]:
         if motors is None:
             return list(self.motors)
         elif isinstance(motors, str):
             return [motors]
-        elif isinstance(motors, list):
-            return motors.copy()
+        elif isinstance(motors, int):
+            return [self._id_to_name(motors)]
+        elif isinstance(motors, Sequence):
+            return [m if isinstance(m, str) else self._id_to_name(m) for m in motors]
         else:
             raise TypeError(motors)
 
-    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> list[str]:
+    def _get_ids_values_dict(self, values: Value | dict[str, Value] | None) -> dict[int, Value]:
         if isinstance(values, (int | float)):
             return dict.fromkeys(self.ids, values)
         elif isinstance(values, dict):
@@ -640,18 +649,19 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     @abc.abstractmethod
-    def enable_torque(self, motors: str | list[str] | None = None, num_retry: int = 0) -> None:
+    def enable_torque(self, motors: int | str | list[str] | None = None, num_retry: int = 0) -> None:
         """Enable torque on selected motors.
 
         Args:
-            motor (int): Same semantics as :pymeth:`disable_torque`. Defaults to `None`.
+            motors (int | str | list[str] | None, optional): Same semantics as :pymeth:`disable_torque`.
+                Defaults to `None`.
             num_retry (int, optional): Number of additional retry attempts on communication failure.
                 Defaults to 0.
         """
         pass
 
     @contextmanager
-    def torque_disabled(self, motors: int | str | list[str] | None = None):
+    def torque_disabled(self, motors: str | list[str] | None = None):
         """Context-manager that guarantees torque is re-enabled.
 
         This helper is useful to temporarily disable torque when configuring motors.
@@ -728,24 +738,19 @@ class SerialMotorsBus(MotorsBusBase):
         """
         pass
 
-    def reset_calibration(self, motors: NameOrID | list[NameOrID] | None = None) -> None:
+    def reset_calibration(self, motors: NameOrID | Sequence[NameOrID] | None = None) -> None:
         """Restore factory calibration for the selected motors.
 
         Homing offset is set to ``0`` and min/max position limits are set to the full usable range.
         The in-memory :pyattr:`calibration` is cleared.
 
         Args:
-            motors (NameOrID | list[NameOrID] | None, optional): Selection of motors. `None` (default)
+            motors (NameOrID | Sequence[NameOrID] | None, optional): Selection of motors. `None` (default)
                 resets every motor.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        for motor in motors:
+        for motor in motor_names:
             model = self._get_motor_model(motor)
             max_res = self.model_resolution_table[model] - 1
             self.write("Homing_Offset", motor, 0, normalize=False)
@@ -754,7 +759,9 @@ class SerialMotorsBus(MotorsBusBase):
 
         self.calibration = {}
 
-    def set_half_turn_homings(self, motors: NameOrID | list[NameOrID] | None = None) -> dict[NameOrID, Value]:
+    def set_half_turn_homings(
+        self, motors: NameOrID | Sequence[NameOrID] | None = None
+    ) -> dict[NameOrID, Value]:
         """Centre each motor range around its current position.
 
         The function computes and writes a homing offset such that the present position becomes exactly one
@@ -764,17 +771,12 @@ class SerialMotorsBus(MotorsBusBase):
             motors (NameOrID | list[NameOrID] | None, optional): Motors to adjust. Defaults to all motors (`None`).
 
         Returns:
-            dict[NameOrID, Value]: Mapping *motor → written homing offset*.
+            dict[str, Value]: Mapping *motor name → written homing offset*.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        self.reset_calibration(motors)
-        actual_positions = self.sync_read("Present_Position", motors, normalize=False)
+        self.reset_calibration(motor_names)
+        actual_positions = self.sync_read("Present_Position", motor_names, normalize=False)
         homing_offsets = self._get_half_turn_homings(actual_positions)
         for motor, offset in homing_offsets.items():
             self.write("Homing_Offset", motor, offset)
@@ -786,8 +788,8 @@ class SerialMotorsBus(MotorsBusBase):
         pass
 
     def record_ranges_of_motion(
-        self, motors: NameOrID | list[NameOrID] | None = None, display_values: bool = True
-    ) -> tuple[dict[NameOrID, Value], dict[NameOrID, Value]]:
+        self, motors: NameOrID | Sequence[NameOrID] | None = None, display_values: bool = True
+    ) -> tuple[dict[str, Value], dict[str, Value]]:
         """Interactively record the min/max encoder values of each motor.
 
         Move the joints by hand (with torque disabled) while the method streams live positions. Press
@@ -799,30 +801,25 @@ class SerialMotorsBus(MotorsBusBase):
             display_values (bool, optional): When `True` (default) a live table is printed to the console.
 
         Returns:
-            tuple[dict[NameOrID, Value], dict[NameOrID, Value]]: Two dictionaries *mins* and *maxes* with the
+            tuple[dict[str, Value], dict[str, Value]]: Two dictionaries *mins* and *maxes* with the
                 extreme values observed for each motor.
         """
-        if motors is None:
-            motors = list(self.motors)
-        elif isinstance(motors, (str | int)):
-            motors = [motors]
-        elif not isinstance(motors, list):
-            raise TypeError(motors)
+        motor_names = self._get_motors_list(motors)
 
-        start_positions = self.sync_read("Present_Position", motors, normalize=False)
+        start_positions = self.sync_read("Present_Position", motor_names, normalize=False)
         mins = start_positions.copy()
         maxes = start_positions.copy()
 
         user_pressed_enter = False
         while not user_pressed_enter:
-            positions = self.sync_read("Present_Position", motors, normalize=False)
+            positions = self.sync_read("Present_Position", motor_names, normalize=False)
             mins = {motor: min(positions[motor], min_) for motor, min_ in mins.items()}
             maxes = {motor: max(positions[motor], max_) for motor, max_ in maxes.items()}
 
             if display_values:
                 print("\n-------------------------------------------")
                 print(f"{'NAME':<15} | {'MIN':>6} | {'POS':>6} | {'MAX':>6}")
-                for motor in motors:
+                for motor in motor_names:
                     print(f"{motor:<15} | {mins[motor]:>6} | {positions[motor]:>6} | {maxes[motor]:>6}")
 
             if enter_pressed():
@@ -830,9 +827,9 @@ class SerialMotorsBus(MotorsBusBase):
 
             if display_values and not user_pressed_enter:
                 # Move cursor up to overwrite the previous output
-                move_cursor_up(len(motors) + 3)
+                move_cursor_up(len(motor_names) + 3)
 
-        same_min_max = [motor for motor in motors if mins[motor] == maxes[motor]]
+        same_min_max = [motor for motor in motor_names if mins[motor] == maxes[motor]]
         if same_min_max:
             raise ValueError(f"Some motors have the same min and max values:\n{pformat(same_min_max)}")
 
@@ -955,12 +952,12 @@ class SerialMotorsBus(MotorsBusBase):
             if raise_on_error:
                 raise ConnectionError(self.packet_handler.getTxRxResult(comm))
             else:
-                return
+                return None
         if self._is_error(error):
             if raise_on_error:
                 raise RuntimeError(self.packet_handler.getRxPacketError(error))
             else:
-                return
+                return None
 
         return model_number
 
@@ -1007,12 +1004,13 @@ class SerialMotorsBus(MotorsBusBase):
         err_msg = f"Failed to read '{data_name}' on {id_=} after {num_retry + 1} tries."
         value, _, _ = self._read(addr, length, id_, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
 
-        id_value = self._decode_sign(data_name, {id_: value})
+        decoded = self._decode_sign(data_name, {id_: value})
 
         if normalize and data_name in self.normalized_data:
-            id_value = self._normalize(id_value)
+            normalized = self._normalize(decoded)
+            return normalized[id_]
 
-        return id_value[id_]
+        return decoded[id_]
 
     def _read(
         self,
@@ -1023,7 +1021,7 @@ class SerialMotorsBus(MotorsBusBase):
         num_retry: int = 0,
         raise_on_error: bool = True,
         err_msg: str = "",
-    ) -> tuple[int, int]:
+    ) -> tuple[int, int, int]:
         if length == 1:
             read_fn = self.packet_handler.read1ByteTxRx
         elif length == 2:
@@ -1073,13 +1071,14 @@ class SerialMotorsBus(MotorsBusBase):
         model = self.motors[motor].model
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
+        int_value = int(value)
         if normalize and data_name in self.normalized_data:
-            value = self._unnormalize({id_: value})[id_]
+            int_value = self._unnormalize({id_: value})[id_]
 
-        value = self._encode_sign(data_name, {id_: value})[id_]
+        int_value = self._encode_sign(data_name, {id_: int_value})[id_]
 
-        err_msg = f"Failed to write '{data_name}' on {id_=} with '{value}' after {num_retry + 1} tries."
-        self._write(addr, length, id_, value, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
+        err_msg = f"Failed to write '{data_name}' on {id_=} with '{int_value}' after {num_retry + 1} tries."
+        self._write(addr, length, id_, int_value, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
 
     def _write(
         self,
@@ -1113,7 +1112,7 @@ class SerialMotorsBus(MotorsBusBase):
     def sync_read(
         self,
         data_name: str,
-        motors: str | list[str] | None = None,
+        motors: NameOrID | Sequence[NameOrID] | None = None,
         *,
         normalize: bool = True,
         num_retry: int = 0,
@@ -1122,7 +1121,7 @@ class SerialMotorsBus(MotorsBusBase):
 
         Args:
             data_name (str): Register name.
-            motors (str | list[str] | None, optional): Motors to query. `None` (default) reads every motor.
+            motors (NameOrID | Sequence[NameOrID] | None, optional): Motors to query. `None` (default) reads every motor.
             normalize (bool, optional): Normalisation flag.  Defaults to `True`.
             num_retry (int, optional): Retry attempts.  Defaults to `0`.
 
@@ -1143,16 +1142,17 @@ class SerialMotorsBus(MotorsBusBase):
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
         err_msg = f"Failed to sync read '{data_name}' on {ids=} after {num_retry + 1} tries."
-        ids_values, _ = self._sync_read(
+        raw_ids_values, _ = self._sync_read(
             addr, length, ids, num_retry=num_retry, raise_on_error=True, err_msg=err_msg
         )
 
-        ids_values = self._decode_sign(data_name, ids_values)
+        decoded = self._decode_sign(data_name, raw_ids_values)
 
         if normalize and data_name in self.normalized_data:
-            ids_values = self._normalize(ids_values)
+            normalized = self._normalize(decoded)
+            return {self._id_to_name(id_): value for id_, value in normalized.items()}
 
-        return {self._id_to_name(id_): value for id_, value in ids_values.items()}
+        return {self._id_to_name(id_): value for id_, value in decoded.items()}
 
     def _sync_read(
         self,
@@ -1224,21 +1224,24 @@ class SerialMotorsBus(MotorsBusBase):
             num_retry (int, optional): Retry attempts.  Defaults to `0`.
         """
 
-        ids_values = self._get_ids_values_dict(values)
-        models = [self._id_to_model(id_) for id_ in ids_values]
+        raw_ids_values = self._get_ids_values_dict(values)
+        models = [self._id_to_model(id_) for id_ in raw_ids_values]
         if self._has_different_ctrl_tables:
             assert_same_address(self.model_ctrl_table, models, data_name)
 
         model = next(iter(models))
         addr, length = get_address(self.model_ctrl_table, model, data_name)
 
+        int_ids_values = {id_: int(val) for id_, val in raw_ids_values.items()}
         if normalize and data_name in self.normalized_data:
-            ids_values = self._unnormalize(ids_values)
+            int_ids_values = self._unnormalize(raw_ids_values)
 
-        ids_values = self._encode_sign(data_name, ids_values)
+        int_ids_values = self._encode_sign(data_name, int_ids_values)
 
-        err_msg = f"Failed to sync write '{data_name}' with {ids_values=} after {num_retry + 1} tries."
-        self._sync_write(addr, length, ids_values, num_retry=num_retry, raise_on_error=True, err_msg=err_msg)
+        err_msg = f"Failed to sync write '{data_name}' with ids_values={int_ids_values} after {num_retry + 1} tries."
+        self._sync_write(
+            addr, length, int_ids_values, num_retry=num_retry, raise_on_error=True, err_msg=err_msg
+        )
 
     def _sync_write(
         self,

src/lerobot/policies/act/configuration_act.py

@@ -28,7 +28,7 @@ class ACTConfig(PreTrainedConfig):
     Defaults are configured for training on bimanual Aloha tasks like "insertion" or "transfer".
 
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
-    Those are: `input_shapes` and 'output_shapes`.
+    Those are: `input_features` and `output_features`.
 
     Notes on the inputs and outputs:
         - Either:
@@ -48,21 +48,12 @@ class ACTConfig(PreTrainedConfig):
             This should be no greater than the chunk size. For example, if the chunk size size 100, you may
             set this to 50. This would mean that the model predicts 100 steps worth of actions, runs 50 in the
             environment, and throws the other 50 out.
-        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
-            the input data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
-            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
-            include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
-            the output data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
-            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
-        input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
-            and the value specifies the normalization mode to apply. The two available modes are "mean_std"
-            which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
-            [-1, 1] range.
-        output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
-            original scale. Note that this is also used for normalizing the training targets.
+        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
+            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
+            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
+            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
         pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone.
             `None` means no pretrained weights.

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -30,7 +30,7 @@ class DiffusionConfig(PreTrainedConfig):
     Defaults are configured for training with PushT providing proprioceptive and single camera observations.
 
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
-    Those are: `input_shapes` and `output_shapes`.
+    Those are: `input_features` and `output_features`.
 
     Notes on the inputs and outputs:
         - "observation.state" is required as an input key.
@@ -48,21 +48,12 @@ class DiffusionConfig(PreTrainedConfig):
         horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
         n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
             See `DiffusionPolicy.select_action` for more details.
-        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
-            the input data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
-            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
-            include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
-            the output data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
-            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
-        input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
-            and the value specifies the normalization mode to apply. The two available modes are "mean_std"
-            which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
-            [-1, 1] range.
-        output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
-            original scale. Note that this is also used for normalizing the training targets.
+        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
+            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
+            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
+            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
         crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
             within the image size. If None, no cropping is done.
@@ -73,7 +64,7 @@ class DiffusionConfig(PreTrainedConfig):
         use_group_norm: Whether to replace batch normalization with group normalization in the backbone.
             The group sizes are set to be about 16 (to be precise, feature_dim // 16).
         spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax.
-        use_separate_rgb_encoders_per_camera: Whether to use a separate RGB encoder for each camera view.
+        use_separate_rgb_encoder_per_camera: Whether to use a separate RGB encoder for each camera view.
         down_dims: Feature dimension for each stage of temporal downsampling in the diffusion modeling Unet.
             You may provide a variable number of dimensions, therefore also controlling the degree of
             downsampling.

src/lerobot/policies/sac/modeling_sac.py

@@ -239,8 +239,10 @@ class SACPolicy(
                     + target_param.data * (1.0 - self.config.critic_target_update_weight)
                 )
 
-    def update_temperature(self):
-        self.temperature = self.log_alpha.exp().item()
+    @property
+    def temperature(self) -> float:
+        """Return the current temperature value, always in sync with log_alpha."""
+        return self.log_alpha.exp().item()
 
     def compute_loss_critic(
         self,
@@ -457,11 +459,10 @@ class SACPolicy(
             dim = continuous_action_dim + (1 if self.config.num_discrete_actions is not None else 0)
             self.target_entropy = -np.prod(dim) / 2
 
-    def _init_temperature(self):
-        """Set up temperature parameter and initial log_alpha."""
+    def _init_temperature(self) -> None:
+        """Set up temperature parameter (log_alpha)."""
         temp_init = self.config.temperature_init
         self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)]))
-        self.temperature = self.log_alpha.exp().item()
 
 
 class SACObservationEncoder(nn.Module):

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -378,16 +378,16 @@ class SmolVLAPolicy(PreTrainedPolicy):
         actions_is_pad = batch.get("actions_id_pad")
         loss_dict = {}
         losses = self.model.forward(images, img_masks, lang_tokens, lang_masks, state, actions, noise, time)
-        loss_dict["losses_after_forward"] = losses.clone()
+        loss_dict["losses_after_forward"] = losses.clone().mean().item()
 
         if actions_is_pad is not None:
             in_episode_bound = ~actions_is_pad
             losses = losses * in_episode_bound.unsqueeze(-1)
-            loss_dict["losses_after_in_ep_bound"] = losses.clone()
+            loss_dict["losses_after_in_ep_bound"] = losses.clone().mean().item()
 
         # Remove padding
         losses = losses[:, :, : self.config.max_action_dim]
-        loss_dict["losses_after_rm_padding"] = losses.clone()
+        loss_dict["losses_after_rm_padding"] = losses.clone().mean().item()
 
         if reduction == "none":
             # Return per-sample losses (B,) by averaging over time and action dims

src/lerobot/policies/tdmpc/configuration_tdmpc.py

@@ -30,7 +30,7 @@ class TDMPCConfig(PreTrainedConfig):
     camera observations.
 
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
-    Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift_ratio`.
+    Those are: `input_features`, `output_features`, and perhaps `max_random_shift_ratio`.
 
     Args:
         n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google
@@ -40,24 +40,12 @@ class TDMPCConfig(PreTrainedConfig):
             is an alternative to using action repeats. If this is set to more than 1, then we require
             `n_action_repeats == 1`, `use_mpc == True` and `n_action_steps <= horizon`. Note that this
             approach of using multiple steps from the plan is not in the original implementation.
-        input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
-            the input data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96],
-            indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't
-            include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents
-            the output data name, and the value is a list indicating the dimensions of the corresponding data.
-            For example, "action" refers to an output shape of [14], indicating 14-dimensional actions.
-            Importantly, `output_shapes` doesn't include batch dimension or temporal dimension.
-        input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
-            and the value specifies the normalization mode to apply. The two available modes are "mean_std"
-            which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
-            [-1, 1] range. Note that here this defaults to None meaning inputs are not normalized. This is to
-            match the original implementation.
-        output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
-            original scale. Note that this is also used for normalizing the training targets. NOTE: Clipping
-            to [-1, +1] is used during MPPI/CEM. Therefore, it is recommended that you stick with "min_max"
-            normalization mode here.
+        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
+            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
+            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
+            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         image_encoder_hidden_dim: Number of channels for the convolutional layers used for image encoding.
         state_encoder_hidden_dim: Hidden dimension for MLP used for state vector encoding.
         latent_dim: Observation's latent embedding dimension.

src/lerobot/policies/vqbet/configuration_vqbet.py

@@ -32,7 +32,7 @@ class VQBeTConfig(PreTrainedConfig):
     Defaults are configured for training with PushT providing proprioceptive and single camera observations.
 
     The parameters you will most likely need to change are the ones which depend on the environment / sensors.
-    Those are: `input_shapes` and `output_shapes`.
+    Those are: `input_features` and `output_features`.
 
     Notes on the inputs and outputs:
         - "observation.state" is required as an input key.
@@ -46,21 +46,12 @@ class VQBeTConfig(PreTrainedConfig):
             current step and additional steps going back).
         n_action_pred_token: Total number of current token and future tokens that VQ-BeT predicts.
         action_chunk_size: Action chunk size of each action prediction token.
-        input_shapes: A dictionary defining the shapes of the input data for the policy.
-            The key represents the input data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "observation.image" refers to an input from
-            a camera with dimensions [3, 96, 96], indicating it has three color channels and 96x96 resolution.
-            Importantly, shapes doesnt include batch dimension or temporal dimension.
-        output_shapes: A dictionary defining the shapes of the output data for the policy.
-            The key represents the output data name, and the value is a list indicating the dimensions
-            of the corresponding data. For example, "action" refers to an output shape of [14], indicating
-            14-dimensional actions. Importantly, shapes doesnt include batch dimension or temporal dimension.
-        input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"),
-            and the value specifies the normalization mode to apply. The two available modes are "mean_std"
-            which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a
-            [-1, 1] range.
-        output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the
-            original scale. Note that this is also used for normalizing the training targets.
+        input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents
+            the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents
+            the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes.
+        normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to
+            a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX)
         vision_backbone: Name of the torchvision resnet backbone to use for encoding images.
         crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit
             within the image size. If None, no cropping is done.

src/lerobot/processor/converters.py

@@ -168,11 +168,12 @@ def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]:
     """
     pad_keys = {k: v for k, v in batch.items() if "_is_pad" in k}
     task_key = {"task": batch["task"]} if "task" in batch else {}
+    subtask_key = {"subtask": batch["subtask"]} if "subtask" in batch else {}
     index_key = {"index": batch["index"]} if "index" in batch else {}
     task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {}
     episode_index_key = {"episode_index": batch["episode_index"]} if "episode_index" in batch else {}
 
-    return {**pad_keys, **task_key, **index_key, **task_index_key, **episode_index_key}
+    return {**pad_keys, **task_key, **subtask_key, **index_key, **task_index_key, **episode_index_key}
 
 
 def create_transition(

src/lerobot/processor/env_processor.py

@@ -17,7 +17,7 @@ from dataclasses import dataclass
 
 import torch
 
-from lerobot.configs.types import PipelineFeatureType, PolicyFeature
+from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
 from lerobot.utils.constants import OBS_IMAGES, OBS_PREFIX, OBS_STATE, OBS_STR
 
 from .pipeline import ObservationProcessorStep, ProcessorStepRegistry
@@ -92,7 +92,7 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
         # copy over non-STATE features
         for ft, feats in features.items():
-            if ft != PipelineFeatureType.STATE:
+            if ft != FeatureType.STATE:
                 new_features[ft] = feats.copy()
 
         # rebuild STATE features
@@ -100,13 +100,11 @@ class LiberoProcessorStep(ObservationProcessorStep):
 
         # add our new flattened state
         state_feats[OBS_STATE] = PolicyFeature(
-            key=OBS_STATE,
+            type=FeatureType.STATE,
             shape=(8,),  # [eef_pos(3), axis_angle(3), gripper(2)]
-            dtype="float32",
-            description=("Concatenated end-effector position (3), axis-angle (3), and gripper qpos (2)."),
         )
 
-        new_features[PipelineFeatureType.STATE] = state_feats
+        new_features[FeatureType.STATE] = state_feats
 
         return new_features
 

src/lerobot/processor/hil_processor.py

@@ -18,16 +18,18 @@
 import math
 import time
 from dataclasses import dataclass
-from typing import Any, Protocol, TypeVar, runtime_checkable
+from typing import TYPE_CHECKING, Any, Protocol, TypeVar, runtime_checkable
 
 import numpy as np
 import torch
 import torchvision.transforms.functional as F  # noqa: N812
 
 from lerobot.configs.types import PipelineFeatureType, PolicyFeature
-from lerobot.teleoperators.teleoperator import Teleoperator
 from lerobot.teleoperators.utils import TeleopEvents
 
+if TYPE_CHECKING:
+    from lerobot.teleoperators.teleoperator import Teleoperator
+
 from .core import EnvTransition, PolicyAction, TransitionKey
 from .pipeline import (
     ComplementaryDataProcessorStep,
@@ -69,10 +71,10 @@ class HasTeleopEvents(Protocol):
 
 
 # Type variable constrained to Teleoperator subclasses that also implement events
-TeleopWithEvents = TypeVar("TeleopWithEvents", bound=Teleoperator)
+TeleopWithEvents = TypeVar("TeleopWithEvents", bound="Teleoperator")
 
 
-def _check_teleop_with_events(teleop: Teleoperator) -> None:
+def _check_teleop_with_events(teleop: "Teleoperator") -> None:
     """
     Runtime check that a teleoperator implements the `HasTeleopEvents` protocol.
 
@@ -103,7 +105,7 @@ class AddTeleopActionAsComplimentaryDataStep(ComplementaryDataProcessorStep):
         teleop_device: The teleoperator instance to get the action from.
     """
 
-    teleop_device: Teleoperator
+    teleop_device: "Teleoperator"
 
     def complementary_data(self, complementary_data: dict) -> dict:
         """
@@ -312,7 +314,7 @@ class TimeLimitProcessorStep(TruncatedProcessorStep):
 
 @dataclass
 @ProcessorStepRegistry.register("gripper_penalty_processor")
-class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep):
+class GripperPenaltyProcessorStep(ProcessorStep):
     """
     Applies a penalty for inefficient gripper usage.
 
@@ -327,26 +329,27 @@ class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep):
     penalty: float = -0.01
     max_gripper_pos: float = 30.0
 
-    def complementary_data(self, complementary_data: dict) -> dict:
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
         """
         Calculates the gripper penalty and adds it to the complementary data.
 
         Args:
-            complementary_data: The incoming complementary data, which should contain
-                                raw joint positions.
+            transition: The incoming environment transition.
 
         Returns:
-            A new complementary data dictionary with the `discrete_penalty` key added.
+            The modified transition with the penalty added to complementary data.
         """
-        action = self.transition.get(TransitionKey.ACTION)
+        new_transition = transition.copy()
+        action = new_transition.get(TransitionKey.ACTION)
+        complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
 
         raw_joint_positions = complementary_data.get("raw_joint_positions")
         if raw_joint_positions is None:
-            return complementary_data
+            return new_transition
 
         current_gripper_pos = raw_joint_positions.get(GRIPPER_KEY, None)
         if current_gripper_pos is None:
-            return complementary_data
+            return new_transition
 
         # Gripper action is a PolicyAction at this stage
         gripper_action = action[-1].item()
@@ -362,11 +365,12 @@ class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep):
 
         gripper_penalty = self.penalty * int(gripper_penalty_bool)
 
-        # Create new complementary data with penalty info
+        # Update complementary data with penalty info
         new_complementary_data = dict(complementary_data)
         new_complementary_data[DISCRETE_PENALTY_KEY] = gripper_penalty
+        new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data
 
-        return new_complementary_data
+        return new_transition
 
     def get_config(self) -> dict[str, Any]:
         """

src/lerobot/processor/tokenizer_processor.py

@@ -34,6 +34,8 @@ from lerobot.utils.constants import (
     ACTION_TOKEN_MASK,
     ACTION_TOKENS,
     OBS_LANGUAGE_ATTENTION_MASK,
+    OBS_LANGUAGE_SUBTASK_ATTENTION_MASK,
+    OBS_LANGUAGE_SUBTASK_TOKENS,
     OBS_LANGUAGE_TOKENS,
 )
 from lerobot.utils.import_utils import _transformers_available
@@ -139,6 +141,32 @@ class TokenizerProcessorStep(ObservationProcessorStep):
 
         return None
 
+    def get_subtask(self, transition: EnvTransition) -> list[str] | None:
+        """
+        Extracts the subtask from the transition's complementary data.
+
+        Args:
+            transition: The environment transition.
+
+        Returns:
+            A list of subtask strings, or None if the subtask key is not found or the value is None.
+        """
+        complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
+        if complementary_data is None:
+            return None
+
+        subtask = complementary_data.get("subtask")
+        if subtask is None:
+            return None
+
+        # Standardize to a list of strings for the tokenizer
+        if isinstance(subtask, str):
+            return [subtask]
+        elif isinstance(subtask, list) and all(isinstance(t, str) for t in subtask):
+            return subtask
+
+        return None
+
     def observation(self, observation: RobotObservation) -> RobotObservation:
         """
         Tokenizes the task description and adds it to the observation dictionary.
@@ -176,6 +204,24 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         new_observation[OBS_LANGUAGE_TOKENS] = tokenized_prompt["input_ids"]
         new_observation[OBS_LANGUAGE_ATTENTION_MASK] = tokenized_prompt["attention_mask"].to(dtype=torch.bool)
 
+        # Tokenize subtask if available
+        subtask = self.get_subtask(self.transition)
+        if subtask is not None:
+            tokenized_subtask = self._tokenize_text(subtask)
+
+            # Move new tokenized tensors to the detected device
+            if target_device is not None:
+                tokenized_subtask = {
+                    k: v.to(target_device) if isinstance(v, torch.Tensor) else v
+                    for k, v in tokenized_subtask.items()
+                }
+
+            # Add tokenized subtask to the observation
+            new_observation[OBS_LANGUAGE_SUBTASK_TOKENS] = tokenized_subtask["input_ids"]
+            new_observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] = tokenized_subtask["attention_mask"].to(
+                dtype=torch.bool
+            )
+
         return new_observation
 
     def _detect_device(self, transition: EnvTransition) -> torch.device | None:

src/lerobot/rl/gym_manipulator.py

@@ -412,7 +412,10 @@ def make_processors(
         if cfg.processor.observation.add_current_to_observation:
             env_pipeline_steps.append(MotorCurrentProcessorStep(robot=env.robot))
 
-    if kinematics_solver is not None:
+    add_ee_pose = (
+        cfg.processor.observation is not None and cfg.processor.observation.add_ee_pose_to_observation
+    )
+    if kinematics_solver is not None and add_ee_pose:
         env_pipeline_steps.append(
             ForwardKinematicsJointsToEEObservation(
                 kinematics=kinematics_solver,
@@ -435,7 +438,12 @@ def make_processors(
         )
 
     # Add gripper penalty processor if gripper config exists and enabled
-    if cfg.processor.gripper is not None and cfg.processor.gripper.use_gripper:
+    # Only add if max_gripper_pos is explicitly configured (required for normalization)
+    if (
+        cfg.processor.gripper is not None
+        and cfg.processor.gripper.use_gripper
+        and cfg.processor.max_gripper_pos is not None
+    ):
         env_pipeline_steps.append(
             GripperPenaltyProcessorStep(
                 penalty=cfg.processor.gripper.gripper_penalty,

src/lerobot/rl/learner.py

@@ -545,9 +545,6 @@ def add_actor_information_and_train(
                 training_infos["temperature_grad_norm"] = temp_grad_norm
                 training_infos["temperature"] = policy.temperature
 
-                # Update temperature
-                policy.update_temperature()
-
         # Push policy to actors if needed
         if time.time() - last_time_policy_pushed > policy_parameters_push_frequency:
             push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy)

src/lerobot/rl/wandb_utils.py

@@ -26,8 +26,21 @@ from lerobot.configs.train import TrainPipelineConfig
 from lerobot.utils.constants import PRETRAINED_MODEL_DIR
 
 
-def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str:
+def cfg_to_group(
+    cfg: TrainPipelineConfig, return_list: bool = False, truncate_tags: bool = False, max_tag_length: int = 64
+) -> list[str] | str:
     """Return a group name for logging. Optionally returns group name as list."""
+
+    def _maybe_truncate(tag: str) -> str:
+        """Truncate tag to max_tag_length characters if required.
+
+        wandb rejects tags longer than 64 characters.
+        See: https://github.com/wandb/wandb/blob/main/wandb/sdk/wandb_settings.py
+        """
+        if len(tag) <= max_tag_length:
+            return tag
+        return tag[:max_tag_length]
+
     lst = [
         f"policy:{cfg.policy.type}",
         f"seed:{cfg.seed}",
@@ -36,6 +49,8 @@ def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[st
         lst.append(f"dataset:{cfg.dataset.repo_id}")
     if cfg.env is not None:
         lst.append(f"env:{cfg.env.type}")
+    if truncate_tags:
+        lst = [_maybe_truncate(tag) for tag in lst]
     return lst if return_list else "-".join(lst)
 
 
@@ -83,7 +98,7 @@ class WandBLogger:
             entity=self.cfg.entity,
             name=self.job_name,
             notes=self.cfg.notes,
-            tags=cfg_to_group(cfg, return_list=True),
+            tags=cfg_to_group(cfg, return_list=True, truncate_tags=True),
             dir=self.log_dir,
             config=cfg.to_dict(),
             # TODO(rcadene): try set to True

src/lerobot/robots/bi_openarm_follower/__init__.py

@@ -0,0 +1,20 @@
+#!/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.
+
+from .bi_openarm_follower import BiOpenArmFollower
+from .config_bi_openarm_follower import BiOpenArmFollowerConfig
+
+__all__ = ["BiOpenArmFollower", "BiOpenArmFollowerConfig"]

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -0,0 +1,180 @@
+#!/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 logging
+from functools import cached_property
+
+from lerobot.processor import RobotAction, RobotObservation
+from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+from ..robot import Robot
+from .config_bi_openarm_follower import BiOpenArmFollowerConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BiOpenArmFollower(Robot):
+    """
+    Bimanual OpenArm Follower Arms
+    """
+
+    config_class = BiOpenArmFollowerConfig
+    name = "bi_openarm_follower"
+
+    def __init__(self, config: BiOpenArmFollowerConfig):
+        super().__init__(config)
+        self.config = config
+
+        left_arm_config = OpenArmFollowerConfig(
+            id=f"{config.id}_left" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.left_arm_config.port,
+            disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
+            max_relative_target=config.left_arm_config.max_relative_target,
+            cameras=config.left_arm_config.cameras,
+            side=config.left_arm_config.side,
+            can_interface=config.left_arm_config.can_interface,
+            use_can_fd=config.left_arm_config.use_can_fd,
+            can_bitrate=config.left_arm_config.can_bitrate,
+            can_data_bitrate=config.left_arm_config.can_data_bitrate,
+            motor_config=config.left_arm_config.motor_config,
+            position_kd=config.left_arm_config.position_kd,
+            position_kp=config.left_arm_config.position_kp,
+            joint_limits=config.left_arm_config.joint_limits,
+        )
+
+        right_arm_config = OpenArmFollowerConfig(
+            id=f"{config.id}_right" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.right_arm_config.port,
+            disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
+            max_relative_target=config.right_arm_config.max_relative_target,
+            cameras=config.right_arm_config.cameras,
+            side=config.right_arm_config.side,
+            can_interface=config.right_arm_config.can_interface,
+            use_can_fd=config.right_arm_config.use_can_fd,
+            can_bitrate=config.right_arm_config.can_bitrate,
+            can_data_bitrate=config.right_arm_config.can_data_bitrate,
+            motor_config=config.right_arm_config.motor_config,
+            position_kd=config.right_arm_config.position_kd,
+            position_kp=config.right_arm_config.position_kp,
+            joint_limits=config.right_arm_config.joint_limits,
+        )
+
+        self.left_arm = OpenArmFollower(left_arm_config)
+        self.right_arm = OpenArmFollower(right_arm_config)
+
+        # Only for compatibility with other parts of the codebase that expect a `robot.cameras` attribute
+        self.cameras = {**self.left_arm.cameras, **self.right_arm.cameras}
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        left_arm_motors_ft = self.left_arm._motors_ft
+        right_arm_motors_ft = self.right_arm._motors_ft
+
+        return {
+            **{f"left_{k}": v for k, v in left_arm_motors_ft.items()},
+            **{f"right_{k}": v for k, v in right_arm_motors_ft.items()},
+        }
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        left_arm_cameras_ft = self.left_arm._cameras_ft
+        right_arm_cameras_ft = self.right_arm._cameras_ft
+
+        return {
+            **{f"left_{k}": v for k, v in left_arm_cameras_ft.items()},
+            **{f"right_{k}": v for k, v in right_arm_cameras_ft.items()},
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.left_arm.is_connected and self.right_arm.is_connected
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        self.left_arm.connect(calibrate)
+        self.right_arm.connect(calibrate)
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
+
+    def calibrate(self) -> None:
+        self.left_arm.calibrate()
+        self.right_arm.calibrate()
+
+    def configure(self) -> None:
+        self.left_arm.configure()
+        self.right_arm.configure()
+
+    def setup_motors(self) -> None:
+        raise NotImplementedError(
+            "Motor ID configuration is typically done via manufacturer tools for CAN motors."
+        )
+
+    @check_if_not_connected
+    def get_observation(self) -> RobotObservation:
+        obs_dict = {}
+
+        # Add "left_" prefix
+        left_obs = self.left_arm.get_observation()
+        obs_dict.update({f"left_{key}": value for key, value in left_obs.items()})
+
+        # Add "right_" prefix
+        right_obs = self.right_arm.get_observation()
+        obs_dict.update({f"right_{key}": value for key, value in right_obs.items()})
+
+        return obs_dict
+
+    @check_if_not_connected
+    def send_action(
+        self,
+        action: RobotAction,
+        custom_kp: dict[str, float] | None = None,
+        custom_kd: dict[str, float] | None = None,
+    ) -> RobotAction:
+        # Remove "left_" prefix
+        left_action = {
+            key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_")
+        }
+        # Remove "right_" prefix
+        right_action = {
+            key.removeprefix("right_"): value for key, value in action.items() if key.startswith("right_")
+        }
+
+        sent_action_left = self.left_arm.send_action(left_action, custom_kp, custom_kd)
+        sent_action_right = self.right_arm.send_action(right_action, custom_kp, custom_kd)
+
+        # Add prefixes back
+        prefixed_sent_action_left = {f"left_{key}": value for key, value in sent_action_left.items()}
+        prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
+
+        return {**prefixed_sent_action_left, **prefixed_sent_action_right}
+
+    @check_if_not_connected
+    def disconnect(self):
+        self.left_arm.disconnect()
+        self.right_arm.disconnect()

src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py

@@ -0,0 +1,30 @@
+#!/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.
+
+from dataclasses import dataclass
+
+from lerobot.robots.openarm_follower import OpenArmFollowerConfigBase
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("bi_openarm_follower")
+@dataclass
+class BiOpenArmFollowerConfig(RobotConfig):
+    """Configuration class for Bi OpenArm Follower robots."""
+
+    left_arm_config: OpenArmFollowerConfigBase
+    right_arm_config: OpenArmFollowerConfigBase

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -19,6 +19,7 @@ from functools import cached_property
 
 from lerobot.processor import RobotAction, RobotObservation
 from lerobot.robots.so_follower import SOFollower, SOFollowerRobotConfig
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot
 from .config_bi_so_follower import BiSOFollowerConfig
@@ -96,6 +97,7 @@ class BiSOFollower(Robot):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
+    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -116,6 +118,7 @@ class BiSOFollower(Robot):
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
+    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         obs_dict = {}
 
@@ -129,6 +132,7 @@ class BiSOFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(self, action: RobotAction) -> RobotAction:
         # Remove "left_" prefix
         left_action = {
@@ -148,6 +152,7 @@ class BiSOFollower(Robot):
 
         return {**prefixed_sent_action_left, **prefixed_sent_action_right}
 
+    @check_if_not_connected
     def disconnect(self):
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/robots/openarm_follower/__init__.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config_openarm_follower import OpenArmFollowerConfig, OpenArmFollowerConfigBase
+from .openarm_follower import OpenArmFollower
+
+__all__ = ["OpenArmFollower", "OpenArmFollowerConfig", "OpenArmFollowerConfigBase"]

src/lerobot/robots/openarm_follower/config_openarm_follower.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+LEFT_DEFAULT_JOINTS_LIMITS: dict[str, tuple[float, float]] = {
+    "joint_1": (-75.0, 75.0),
+    "joint_2": (-90.0, 9.0),
+    "joint_3": (-85.0, 85.0),
+    "joint_4": (0.0, 135.0),
+    "joint_5": (-85.0, 85.0),
+    "joint_6": (-40.0, 40.0),
+    "joint_7": (-80.0, 80.0),
+    "gripper": (-65.0, 0.0),
+}
+
+RIGHT_DEFAULT_JOINTS_LIMITS: dict[str, tuple[float, float]] = {
+    "joint_1": (-75.0, 75.0),
+    "joint_2": (-9.0, 90.0),
+    "joint_3": (-85.0, 85.0),
+    "joint_4": (0.0, 135.0),
+    "joint_5": (-85.0, 85.0),
+    "joint_6": (-40.0, 40.0),
+    "joint_7": (-80.0, 80.0),
+    "gripper": (-65.0, 0.0),
+}
+
+
+@dataclass
+class OpenArmFollowerConfigBase:
+    """Base configuration for the OpenArms follower robot with Damiao motors."""
+
+    # CAN interfaces - one per arm
+    # arm CAN interface (e.g., "can1")
+    # Linux: "can0", "can1", etc.
+    port: str
+
+    # side of the arm: "left" or "right". If "None" default values will be used
+    side: str | None = None
+
+    # CAN interface type: "socketcan" (Linux), "slcan" (serial), or "auto" (auto-detect)
+    can_interface: str = "socketcan"
+
+    # CAN FD settings (OpenArms uses CAN FD by default)
+    use_can_fd: bool = True
+    can_bitrate: int = 1000000  # Nominal bitrate (1 Mbps)
+    can_data_bitrate: int = 5000000  # Data bitrate for CAN FD (5 Mbps)
+
+    # Whether to disable torque when disconnecting
+    disable_torque_on_disconnect: bool = True
+
+    # Safety limit for relative target positions
+    # Set to a positive scalar for all motors, or a dict mapping motor names to limits
+    max_relative_target: float | dict[str, float] | None = None
+
+    # Camera configurations
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)
+
+    # Motor configuration for OpenArms (7 DOF per arm)
+    # Maps motor names to (send_can_id, recv_can_id, motor_type)
+    # Based on: https://docs.openarm.dev/software/setup/configure-test
+    # OpenArms uses 4 types of motors:
+    # - DM8009 (DM-J8009P-2EC) for shoulders (high torque)
+    # - DM4340P and DM4340 for shoulder rotation and elbow
+    # - DM4310 (DM-J4310-2EC V1.1) for wrist and gripper
+    motor_config: dict[str, tuple[int, int, str]] = field(
+        default_factory=lambda: {
+            "joint_1": (0x01, 0x11, "dm8009"),  # J1 - Shoulder pan (DM8009)
+            "joint_2": (0x02, 0x12, "dm8009"),  # J2 - Shoulder lift (DM8009)
+            "joint_3": (0x03, 0x13, "dm4340"),  # J3 - Shoulder rotation (DM4340)
+            "joint_4": (0x04, 0x14, "dm4340"),  # J4 - Elbow flex (DM4340)
+            "joint_5": (0x05, 0x15, "dm4310"),  # J5 - Wrist roll (DM4310)
+            "joint_6": (0x06, 0x16, "dm4310"),  # J6 - Wrist pitch (DM4310)
+            "joint_7": (0x07, 0x17, "dm4310"),  # J7 - Wrist rotation (DM4310)
+            "gripper": (0x08, 0x18, "dm4310"),  # J8 - Gripper (DM4310)
+        }
+    )
+
+    # MIT control parameters for position control (used in send_action)
+    # List of 8 values: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper]
+    position_kp: list[float] = field(
+        default_factory=lambda: [240.0, 240.0, 240.0, 240.0, 24.0, 31.0, 25.0, 25.0]
+    )
+    position_kd: list[float] = field(default_factory=lambda: [5.0, 5.0, 3.0, 5.0, 0.3, 0.3, 0.3, 0.3])
+
+    # Values for joint limits. Can be overridden via CLI (for custom values) or by setting config.side to either 'left' or 'right'.
+    # If config.side is left set to None and no CLI values are passed, the default joint limit values are small for safety.
+    joint_limits: dict[str, tuple[float, float]] = field(
+        default_factory=lambda: {
+            "joint_1": (-5.0, 5.0),
+            "joint_2": (-5.0, 5.0),
+            "joint_3": (-5.0, 5.0),
+            "joint_4": (0.0, 5.0),
+            "joint_5": (-5.0, 5.0),
+            "joint_6": (-5.0, 5.0),
+            "joint_7": (-5.0, 5.0),
+            "gripper": (-5.0, 0.0),
+        }
+    )
+
+
+@RobotConfig.register_subclass("openarm_follower")
+@dataclass
+class OpenArmFollowerConfig(RobotConfig, OpenArmFollowerConfigBase):
+    pass

src/lerobot/robots/openarm_follower/openarm_follower.py

@@ -0,0 +1,343 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from functools import cached_property
+from typing import Any
+
+from lerobot.cameras.utils import make_cameras_from_configs
+from lerobot.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.motors.damiao import DamiaoMotorsBus
+from lerobot.processor import RobotAction, RobotObservation
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_openarm_follower import (
+    LEFT_DEFAULT_JOINTS_LIMITS,
+    RIGHT_DEFAULT_JOINTS_LIMITS,
+    OpenArmFollowerConfig,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class OpenArmFollower(Robot):
+    """
+    OpenArms Follower Robot which uses CAN bus communication to control 7 DOF arm with a gripper.
+    The arm uses Damiao motors in MIT control mode.
+    """
+
+    config_class = OpenArmFollowerConfig
+    name = "openarm_follower"
+
+    def __init__(self, config: OpenArmFollowerConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Arm motors
+        motors: dict[str, Motor] = {}
+        for motor_name, (send_id, recv_id, motor_type_str) in config.motor_config.items():
+            motor = Motor(
+                send_id, motor_type_str, MotorNormMode.DEGREES
+            )  # Always use degrees for Damiao motors
+            motor.recv_id = recv_id
+            motor.motor_type_str = motor_type_str
+            motors[motor_name] = motor
+
+        self.bus = DamiaoMotorsBus(
+            port=self.config.port,
+            motors=motors,
+            calibration=self.calibration,
+            can_interface=self.config.can_interface,
+            use_can_fd=self.config.use_can_fd,
+            bitrate=self.config.can_bitrate,
+            data_bitrate=self.config.can_data_bitrate if self.config.use_can_fd else None,
+        )
+
+        if config.side is not None:
+            if config.side == "left":
+                config.joint_limits = LEFT_DEFAULT_JOINTS_LIMITS
+            elif config.side == "right":
+                config.joint_limits = RIGHT_DEFAULT_JOINTS_LIMITS
+            else:
+                raise ValueError(
+                    "config.side must be either 'left', 'right' (for default values) or 'None' (for CLI values)"
+                )
+        else:
+            logger.info(
+                "Set config.side to either 'left' or 'right' to use pre-configured values for joint limits."
+            )
+        logger.info(f"Values used for joint limits: {config.joint_limits}.")
+
+        # Initialize cameras
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        """Motor features for observation and action spaces."""
+        features: dict[str, type] = {}
+        for motor in self.bus.motors:
+            features[f"{motor}.pos"] = float
+            features[f"{motor}.vel"] = float  # Add this
+            features[f"{motor}.torque"] = float  # Add this
+        return features
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        """Camera features for observation space."""
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        """Combined observation features from motors and cameras."""
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        """Action features."""
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        """Check if robot is connected."""
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        Connect to the robot and optionally calibrate.
+
+        We assume that at connection time, the arms are in a safe rest position,
+        and torque can be safely disabled to run calibration if needed.
+        """
+
+        # Connect to CAN bus
+        logger.info(f"Connecting arm on {self.config.port}...")
+        self.bus.connect()
+
+        # Run calibration if needed
+        if not self.is_calibrated and calibrate:
+            logger.info(
+                "Mismatch between calibration values in the motor and the calibration file or no calibration file found"
+            )
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+
+        if self.is_calibrated:
+            self.bus.set_zero_position()
+
+        self.bus.enable_torque()
+
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        """Check if robot is calibrated."""
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        """
+        Run calibration procedure for OpenArms robot.
+
+        The calibration procedure:
+        1. Disable torque
+        2. Ask user to position arms in hanging position with grippers closed
+        3. Set this as zero position
+        4. Record range of motion for each joint
+        5. Save calibration
+        """
+        if self.calibration:
+            # Calibration file exists, ask user whether to use it or run new calibration
+            user_input = input(
+                f"Press ENTER to use provided calibration file associated with the id {self.id}, or type 'c' and press ENTER to run calibration: "
+            )
+            if user_input.strip().lower() != "c":
+                logger.info(f"Writing calibration file associated with the id {self.id} to the motors")
+                self.bus.write_calibration(self.calibration)
+                return
+
+        logger.info(f"\nRunning calibration for {self}")
+        self.bus.disable_torque()
+
+        # Step 1: Set zero position
+        input(
+            "\nCalibration: Set Zero Position)\n"
+            "Position the arm in the following configuration:\n"
+            "  - Arm hanging straight down\n"
+            "  - Gripper closed\n"
+            "Press ENTER when ready..."
+        )
+
+        # Set current position as zero for all motors
+        self.bus.set_zero_position()
+        logger.info("Arm zero position set.")
+
+        logger.info("Setting range: -90° to +90° for safety by default for all joints")
+        for motor_name, motor in self.bus.motors.items():
+            self.calibration[motor_name] = MotorCalibration(
+                id=motor.id,
+                drive_mode=0,
+                homing_offset=0,
+                range_min=-90,
+                range_max=90,
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print(f"Calibration saved to {self.calibration_fpath}")
+
+    def configure(self) -> None:
+        """Configure motors with appropriate settings."""
+        # TODO(Steven, Pepijn): Slightly different from what it is happening in the leader
+        with self.bus.torque_disabled():
+            self.bus.configure_motors()
+
+    def setup_motors(self) -> None:
+        raise NotImplementedError(
+            "Motor ID configuration is typically done via manufacturer tools for CAN motors."
+        )
+
+    @check_if_not_connected
+    def get_observation(self) -> RobotObservation:
+        """
+        Get current observation from robot including position, velocity, and torque.
+
+        Reads all motor states (pos/vel/torque) in one CAN refresh cycle
+        instead of 3 separate reads.
+        """
+        start = time.perf_counter()
+
+        obs_dict: dict[str, Any] = {}
+
+        states = self.bus.sync_read_all_states()
+
+        for motor in self.bus.motors:
+            state = states.get(motor, {})
+            obs_dict[f"{motor}.pos"] = state.get("position", 0.0)
+            obs_dict[f"{motor}.vel"] = state.get("velocity", 0.0)
+            obs_dict[f"{motor}.torque"] = state.get("torque", 0.0)
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} get_observation took: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    @check_if_not_connected
+    def send_action(
+        self,
+        action: RobotAction,
+        custom_kp: dict[str, float] | None = None,
+        custom_kd: dict[str, float] | None = None,
+    ) -> RobotAction:
+        """
+        Send action command to robot.
+
+        The action magnitude may be clipped based on safety limits.
+
+        Args:
+            action: Dictionary with motor positions (e.g., "joint_1.pos", "joint_2.pos")
+            custom_kp: Optional custom kp gains per motor (e.g., {"joint_1": 120.0, "joint_2": 150.0})
+            custom_kd: Optional custom kd gains per motor (e.g., {"joint_1": 1.5, "joint_2": 2.0})
+
+        Returns:
+            The action actually sent (potentially clipped)
+        """
+
+        goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
+
+        # Apply joint limit clipping to arm
+        for motor_name, position in goal_pos.items():
+            if motor_name in self.config.joint_limits:
+                min_limit, max_limit = self.config.joint_limits[motor_name]
+                clipped_position = max(min_limit, min(max_limit, position))
+                if clipped_position != position:
+                    logger.debug(f"Clipped {motor_name} from {position:.2f}° to {clipped_position:.2f}°")
+                goal_pos[motor_name] = clipped_position
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position")
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
+            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+
+        # TODO(Steven, Pepijn): Refactor writing
+        # Motor name to index mapping for gains
+        motor_index = {
+            "joint_1": 0,
+            "joint_2": 1,
+            "joint_3": 2,
+            "joint_4": 3,
+            "joint_5": 4,
+            "joint_6": 5,
+            "joint_7": 6,
+            "gripper": 7,
+        }
+
+        # Use batch MIT control for arm (sends all commands, then collects responses)
+        commands = {}
+        for motor_name, position_degrees in goal_pos.items():
+            idx = motor_index.get(motor_name, 0)
+            # Use custom gains if provided, otherwise use config defaults
+            if custom_kp is not None and motor_name in custom_kp:
+                kp = custom_kp[motor_name]
+            else:
+                kp = (
+                    self.config.position_kp[idx]
+                    if isinstance(self.config.position_kp, list)
+                    else self.config.position_kp
+                )
+            if custom_kd is not None and motor_name in custom_kd:
+                kd = custom_kd[motor_name]
+            else:
+                kd = (
+                    self.config.position_kd[idx]
+                    if isinstance(self.config.position_kd, list)
+                    else self.config.position_kd
+                )
+            commands[motor_name] = (kp, kd, position_degrees, 0.0, 0.0)
+
+        self.bus._mit_control_batch(commands)
+
+        return {f"{motor}.pos": val for motor, val in goal_pos.items()}
+
+    @check_if_not_connected
+    def disconnect(self):
+        """Disconnect from robot."""
+
+        # Disconnect CAN bus
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+
+        # Disconnect cameras
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -65,3 +65,6 @@ class UnitreeG1Config(RobotConfig):
 
     # Cameras (ZMQ-based remote cameras)
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
+
+    # Compensates for gravity on the unitree's arms using the arm ik solver
+    gravity_compensation: bool = False

src/lerobot/robots/unitree_g1/g1_utils.py

@@ -18,7 +18,7 @@ from enum import IntEnum
 
 # ruff: noqa: N801, N815
 
-NUM_MOTORS = 35
+NUM_MOTORS = 29
 
 
 class G1_29_JointArmIndex(IntEnum):

src/lerobot/robots/unitree_g1/robot_kinematic_processor.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+import sys
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(parent2_dir)
+
+
+class WeightedMovingFilter:
+    def __init__(self, weights, data_size=14):
+        self._window_size = len(weights)
+        self._weights = np.array(weights)
+        self._data_size = data_size
+        self._filtered_data = np.zeros(self._data_size)
+        self._data_queue = []
+
+    def _apply_filter(self):
+        if len(self._data_queue) < self._window_size:
+            return self._data_queue[-1]
+
+        data_array = np.array(self._data_queue)
+        temp_filtered_data = np.zeros(self._data_size)
+        for i in range(self._data_size):
+            temp_filtered_data[i] = np.convolve(data_array[:, i], self._weights, mode="valid")[-1]
+
+        return temp_filtered_data
+
+    def add_data(self, new_data):
+        assert len(new_data) == self._data_size
+
+        if len(self._data_queue) > 0 and np.array_equal(
+            new_data, self._data_queue[-1]
+        ):  # skip duplicate data
+            return
+
+        if len(self._data_queue) >= self._window_size:
+            self._data_queue.pop(0)
+
+        self._data_queue.append(new_data)
+        self._filtered_data = self._apply_filter()
+
+    @property
+    def filtered_data(self):
+        return self._filtered_data
+
+
+class G1_29_ArmIK:  # noqa: N801
+    def __init__(self, unit_test=False):
+        import casadi
+        import pinocchio as pin
+        from huggingface_hub import snapshot_download
+        from pinocchio import casadi as cpin
+
+        self._pin = pin
+        np.set_printoptions(precision=5, suppress=True, linewidth=200)
+
+        self.unit_test = unit_test
+
+        self.repo_path = snapshot_download("lerobot/unitree-g1-mujoco")
+        urdf_path = os.path.join(self.repo_path, "assets", "g1_body29_hand14.urdf")
+        mesh_dir = os.path.join(self.repo_path, "assets")
+
+        self.robot = self._pin.RobotWrapper.BuildFromURDF(urdf_path, mesh_dir)
+
+        self.mixed_jointsToLockIDs = [
+            "left_hip_pitch_joint",
+            "left_hip_roll_joint",
+            "left_hip_yaw_joint",
+            "left_knee_joint",
+            "left_ankle_pitch_joint",
+            "left_ankle_roll_joint",
+            "right_hip_pitch_joint",
+            "right_hip_roll_joint",
+            "right_hip_yaw_joint",
+            "right_knee_joint",
+            "right_ankle_pitch_joint",
+            "right_ankle_roll_joint",
+            "waist_yaw_joint",
+            "waist_roll_joint",
+            "waist_pitch_joint",
+            "left_hand_thumb_0_joint",
+            "left_hand_thumb_1_joint",
+            "left_hand_thumb_2_joint",
+            "left_hand_middle_0_joint",
+            "left_hand_middle_1_joint",
+            "left_hand_index_0_joint",
+            "left_hand_index_1_joint",
+            "right_hand_thumb_0_joint",
+            "right_hand_thumb_1_joint",
+            "right_hand_thumb_2_joint",
+            "right_hand_index_0_joint",
+            "right_hand_index_1_joint",
+            "right_hand_middle_0_joint",
+            "right_hand_middle_1_joint",
+        ]
+
+        self.reduced_robot = self.robot.buildReducedRobot(
+            list_of_joints_to_lock=self.mixed_jointsToLockIDs,
+            reference_configuration=np.array([0.0] * self.robot.model.nq),
+        )
+
+        # Arm joint names in G1 motor order (G1_29_JointArmIndex)
+        self._arm_joint_names_g1 = [
+            "left_shoulder_pitch_joint",
+            "left_shoulder_roll_joint",
+            "left_shoulder_yaw_joint",
+            "left_elbow_joint",
+            "left_wrist_roll_joint",
+            "left_wrist_pitch_joint",
+            "left_wrist_yaw_joint",
+            "right_shoulder_pitch_joint",
+            "right_shoulder_roll_joint",
+            "right_shoulder_yaw_joint",
+            "right_elbow_joint",
+            "right_wrist_roll_joint",
+            "right_wrist_pitch_joint",
+            "right_wrist_yaw_joint",
+        ]
+        # Pinocchio uses its own joint order in q; build index mapping.
+        self._arm_joint_names_pin = sorted(
+            self._arm_joint_names_g1,
+            key=lambda name: self.reduced_robot.model.idx_qs[self.reduced_robot.model.getJointId(name)],
+        )
+        logger.info(f"Pinocchio arm joint order: {self._arm_joint_names_pin}")
+        self._arm_reorder_g1_to_pin = [
+            self._arm_joint_names_g1.index(name) for name in self._arm_joint_names_pin
+        ]
+        # Inverse mapping to return tau in G1 motor order.
+        self._arm_reorder_pin_to_g1 = np.argsort(self._arm_reorder_g1_to_pin)
+
+        self.reduced_robot.model.addFrame(
+            self._pin.Frame(
+                "L_ee",
+                self.reduced_robot.model.getJointId("left_wrist_yaw_joint"),
+                self._pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T),
+                self._pin.FrameType.OP_FRAME,
+            )
+        )
+
+        self.reduced_robot.model.addFrame(
+            self._pin.Frame(
+                "R_ee",
+                self.reduced_robot.model.getJointId("right_wrist_yaw_joint"),
+                self._pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T),
+                self._pin.FrameType.OP_FRAME,
+            )
+        )
+
+        # Creating Casadi models and data for symbolic computing
+        self.cmodel = cpin.Model(self.reduced_robot.model)
+        self.cdata = self.cmodel.createData()
+
+        # Creating symbolic variables
+        self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1)
+        self.cTf_l = casadi.SX.sym("tf_l", 4, 4)
+        self.cTf_r = casadi.SX.sym("tf_r", 4, 4)
+        cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq)
+
+        # Get the hand joint ID and define the error function
+        self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee")
+        self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee")
+
+        self.translational_error = casadi.Function(
+            "translational_error",
+            [self.cq, self.cTf_l, self.cTf_r],
+            [
+                casadi.vertcat(
+                    self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3],
+                    self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3],
+                )
+            ],
+        )
+        self.rotational_error = casadi.Function(
+            "rotational_error",
+            [self.cq, self.cTf_l, self.cTf_r],
+            [
+                casadi.vertcat(
+                    cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T),
+                    cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T),
+                )
+            ],
+        )
+
+        # Defining the optimization problem
+        self.opti = casadi.Opti()
+        self.var_q = self.opti.variable(self.reduced_robot.model.nq)
+        self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq)  # for smooth
+        self.param_tf_l = self.opti.parameter(4, 4)
+        self.param_tf_r = self.opti.parameter(4, 4)
+        self.translational_cost = casadi.sumsqr(
+            self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r)
+        )
+        self.rotation_cost = casadi.sumsqr(
+            self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r)
+        )
+        self.regularization_cost = casadi.sumsqr(self.var_q)
+        self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last)
+
+        # Setting optimization constraints and goals
+        self.opti.subject_to(
+            self.opti.bounded(
+                self.reduced_robot.model.lowerPositionLimit,
+                self.var_q,
+                self.reduced_robot.model.upperPositionLimit,
+            )
+        )
+        self.opti.minimize(
+            50 * self.translational_cost
+            + self.rotation_cost
+            + 0.02 * self.regularization_cost
+            + 0.1 * self.smooth_cost
+        )
+
+        opts = {
+            "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6},
+            "print_time": False,  # print or not
+            "calc_lam_p": False,  # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F
+        }
+        self.opti.solver("ipopt", opts)
+
+        self.init_data = np.zeros(self.reduced_robot.model.nq)
+        self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14)
+
+    def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
+        if current_lr_arm_motor_q is not None:
+            self.init_data = current_lr_arm_motor_q
+        self.opti.set_initial(self.var_q, self.init_data)
+
+        self.opti.set_value(self.param_tf_l, left_wrist)
+        self.opti.set_value(self.param_tf_r, right_wrist)
+        self.opti.set_value(self.var_q_last, self.init_data)  # for smooth
+
+        try:
+            self.opti.solve()
+
+            sol_q = self.opti.value(self.var_q)
+            self.smooth_filter.add_data(sol_q)
+            sol_q = self.smooth_filter.filtered_data
+
+            if current_lr_arm_motor_dq is not None:
+                v = current_lr_arm_motor_dq * 0.0
+            else:
+                v = (sol_q - self.init_data) * 0.0
+
+            self.init_data = sol_q
+
+            sol_tauff = self._pin.rnea(
+                self.reduced_robot.model,
+                self.reduced_robot.data,
+                sol_q,
+                v,
+                np.zeros(self.reduced_robot.model.nv),
+            )
+
+            return sol_q, sol_tauff
+
+        except Exception as e:
+            logger.error(f"ERROR in convergence, plotting debug info.{e}")
+
+            sol_q = self.opti.debug.value(self.var_q)
+            self.smooth_filter.add_data(sol_q)
+            sol_q = self.smooth_filter.filtered_data
+
+            if current_lr_arm_motor_dq is not None:
+                v = current_lr_arm_motor_dq * 0.0
+            else:
+                v = (sol_q - self.init_data) * 0.0
+
+            self.init_data = sol_q
+
+            logger.error(
+                f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}"
+            )
+
+            return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv)
+
+    def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None):
+        try:
+            q_g1 = np.array(current_lr_arm_motor_q, dtype=float)
+            if q_g1.shape[0] != len(self._arm_joint_names_g1):
+                raise ValueError(f"Expected {len(self._arm_joint_names_g1)} arm joints, got {q_g1.shape[0]}")
+            q_pin = q_g1[self._arm_reorder_g1_to_pin]
+            sol_tauff = self._pin.rnea(
+                self.reduced_robot.model,
+                self.reduced_robot.data,
+                q_pin,
+                np.zeros(self.reduced_robot.model.nv),
+                np.zeros(self.reduced_robot.model.nv),
+            )
+            return sol_tauff[self._arm_reorder_pin_to_g1]
+
+        except Exception as e:
+            logger.error(f"ERROR in convergence, plotting debug info.{e}")
+            return np.zeros(self.reduced_robot.model.nv)

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -27,7 +27,8 @@ import numpy as np
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.envs.factory import make_env
 from lerobot.processor import RobotAction, RobotObservation
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex, G1_29_JointIndex
+from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK
 
 from ..robot import Robot
 from .config_unitree_g1 import UnitreeG1Config
@@ -127,6 +128,8 @@ class UnitreeG1(Robot):
         self.subscribe_thread = None
         self.remote_controller = self.RemoteController()
 
+        self.arm_ik = G1_29_ArmIK()
+
     def _subscribe_motor_state(self):  # polls robot state @ 250Hz
         while not self._shutdown_event.is_set():
             start_time = time.time()
@@ -361,6 +364,20 @@ class UnitreeG1(Robot):
                 self.msg.motor_cmd[motor.value].kd = self.kd[motor.value]
                 self.msg.motor_cmd[motor.value].tau = 0
 
+        if self.config.gravity_compensation:
+            # Build action_np from motor commands (arm joints are indices 15-28, local indices 0-13)
+            action_np = np.zeros(14)
+            arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value  # 15
+            for joint in G1_29_JointArmIndex:
+                local_idx = joint.value - arm_start_idx
+                action_np[local_idx] = self.msg.motor_cmd[joint.value].q
+            tau = self.arm_ik.solve_tau(action_np)
+
+            # Apply tau back to motor commands
+            for joint in G1_29_JointArmIndex:
+                local_idx = joint.value - arm_start_idx
+                self.msg.motor_cmd[joint.value].tau = tau[local_idx]
+
         self.msg.crc = self.crc.Crc(self.msg)
         self.lowcmd_publisher.Write(self.msg)
         return action

src/lerobot/robots/utils.py

@@ -60,6 +60,14 @@ def make_robot_from_config(config: RobotConfig) -> Robot:
         from .reachy2 import Reachy2Robot
 
         return Reachy2Robot(config)
+    elif config.type == "openarm_follower":
+        from .openarm_follower import OpenArmFollower
+
+        return OpenArmFollower(config)
+    elif config.type == "bi_openarm_follower":
+        from .bi_openarm_follower import BiOpenArmFollower
+
+        return BiOpenArmFollower(config)
     elif config.type == "mock_robot":
         from tests.mocks.mock_robot import MockRobot
 

src/lerobot/scripts/lerobot_calibrate.py

@@ -36,23 +36,28 @@ from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraCon
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
+    bi_openarm_follower,
     bi_so_follower,
     hope_jr,
     koch_follower,
     lekiwi,
     make_robot_from_config,
     omx_follower,
+    openarm_follower,
     so_follower,
 )
 from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
+    bi_openarm_leader,
     bi_so_leader,
     homunculus,
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
+    openarm_leader,
     so_leader,
+    unitree_g1,
 )
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.utils import init_logging
@@ -81,8 +86,11 @@ def calibrate(cfg: CalibrateConfig):
         device = make_teleoperator_from_config(cfg.device)
 
     device.connect(calibrate=False)
-    device.calibrate()
-    device.disconnect()
+
+    try:
+        device.calibrate()
+    finally:
+        device.disconnect()
 
 
 def main():

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -18,7 +18,7 @@
 Edit LeRobot datasets using various transformation tools.
 
 This script allows you to delete episodes, split datasets, merge datasets,
-remove features, and convert image datasets to video format.
+remove features, modify tasks, and convert image datasets to video format.
 When new_repo_id is specified, creates a new dataset.
 
 Usage Examples:
@@ -66,6 +66,25 @@ Remove camera feature:
         --operation.type remove_feature \
         --operation.feature_names "['observation.images.top']"
 
+Modify tasks - set a single task for all episodes (WARNING: modifies in-place):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type modify_tasks \
+        --operation.new_task "Pick up the cube and place it"
+
+Modify tasks - set different tasks for specific episodes (WARNING: modifies in-place):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type modify_tasks \
+        --operation.episode_tasks '{"0": "Task A", "1": "Task B", "2": "Task A"}'
+
+Modify tasks - set default task with overrides for specific episodes (WARNING: modifies in-place):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --operation.type modify_tasks \
+        --operation.new_task "Default task" \
+        --operation.episode_tasks '{"5": "Special task for episode 5"}'
+
 Convert image dataset to video format and save locally:
     python -m lerobot.scripts.lerobot_edit_dataset \
         --repo_id lerobot/pusht_image \
@@ -100,6 +119,7 @@ from lerobot.datasets.dataset_tools import (
     convert_image_to_video_dataset,
     delete_episodes,
     merge_datasets,
+    modify_tasks,
     remove_feature,
     split_dataset,
 )
@@ -132,6 +152,13 @@ class RemoveFeatureConfig:
     feature_names: list[str] | None = None
 
 
+@dataclass
+class ModifyTasksConfig:
+    type: str = "modify_tasks"
+    new_task: str | None = None
+    episode_tasks: dict[str, str] | None = None
+
+
 @dataclass
 class ConvertImageToVideoConfig:
     type: str = "convert_image_to_video"
@@ -151,7 +178,12 @@ class ConvertImageToVideoConfig:
 class EditDatasetConfig:
     repo_id: str
     operation: (
-        DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertImageToVideoConfig
+        DeleteEpisodesConfig
+        | SplitConfig
+        | MergeConfig
+        | RemoveFeatureConfig
+        | ModifyTasksConfig
+        | ConvertImageToVideoConfig
     )
     root: str | None = None
     new_repo_id: str | None = None
@@ -296,6 +328,48 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
         LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
 
 
+def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
+    if not isinstance(cfg.operation, ModifyTasksConfig):
+        raise ValueError("Operation config must be ModifyTasksConfig")
+
+    new_task = cfg.operation.new_task
+    episode_tasks_raw = cfg.operation.episode_tasks
+
+    if new_task is None and episode_tasks_raw is None:
+        raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation")
+
+    # Warn about in-place modification behavior
+    if cfg.new_repo_id is not None:
+        logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.")
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.")
+
+    # Convert episode_tasks keys from string to int if needed (CLI passes strings)
+    episode_tasks: dict[int, str] | None = None
+    if episode_tasks_raw is not None:
+        episode_tasks = {int(k): v for k, v in episode_tasks_raw.items()}
+
+    logging.info(f"Modifying tasks in {cfg.repo_id}")
+    if new_task:
+        logging.info(f"  Default task: '{new_task}'")
+    if episode_tasks:
+        logging.info(f"  Episode-specific tasks: {episode_tasks}")
+
+    modified_dataset = modify_tasks(
+        dataset,
+        new_task=new_task,
+        episode_tasks=episode_tasks,
+    )
+
+    logging.info(f"Dataset modified at {dataset.root}")
+    logging.info(f"Tasks: {list(modified_dataset.meta.tasks.index)}")
+
+    if cfg.push_to_hub:
+        logging.info(f"Pushing to hub as {cfg.repo_id}")
+        modified_dataset.push_to_hub()
+
+
 def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
     # Note: Parser may create any config type with the right fields, so we access fields directly
     # instead of checking isinstance()
@@ -371,12 +445,14 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
         handle_merge(cfg)
     elif operation_type == "remove_feature":
         handle_remove_feature(cfg)
+    elif operation_type == "modify_tasks":
+        handle_modify_tasks(cfg)
     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, convert_to_video"
+            f"Available operations: delete_episodes, split, merge, remove_feature, modify_tasks, convert_image_to_video"
         )
 
 

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -44,19 +44,23 @@ import numpy as np
 from lerobot.model.kinematics import RobotKinematics
 from lerobot.robots import (  # noqa: F401
     RobotConfig,
+    bi_openarm_follower,
     bi_so_follower,
     koch_follower,
     make_robot_from_config,
     omx_follower,
+    openarm_follower,
     so_follower,
 )
 from lerobot.teleoperators import (  # noqa: F401
     TeleoperatorConfig,
+    bi_openarm_leader,
     bi_so_leader,
     gamepad,
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
+    openarm_leader,
     so_leader,
 )
 from lerobot.utils.robot_utils import precise_sleep

src/lerobot/scripts/lerobot_record.py

@@ -98,26 +98,31 @@ from lerobot.processor.rename_processor import rename_stats
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
+    bi_openarm_follower,
     bi_so_follower,
     earthrover_mini_plus,
     hope_jr,
     koch_follower,
     make_robot_from_config,
     omx_follower,
+    openarm_follower,
     reachy2,
     so_follower,
-    unitree_g1,
+    unitree_g1 as unitree_g1_robot,
 )
 from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
+    bi_openarm_leader,
     bi_so_leader,
     homunculus,
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
+    openarm_leader,
     reachy2_teleoperator,
     so_leader,
+    unitree_g1,
 )
 from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop
 from lerobot.utils.constants import ACTION, OBS_STR

src/lerobot/scripts/lerobot_replay.py

@@ -53,12 +53,14 @@ from lerobot.processor import (
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
+    bi_openarm_follower,
     bi_so_follower,
     earthrover_mini_plus,
     hope_jr,
     koch_follower,
     make_robot_from_config,
     omx_follower,
+    openarm_follower,
     reachy2,
     so_follower,
     unitree_g1,
@@ -108,25 +110,26 @@ def replay(cfg: ReplayConfig):
 
     robot.connect()
 
-    log_say("Replaying episode", cfg.play_sounds, blocking=True)
-    for idx in range(len(episode_frames)):
-        start_episode_t = time.perf_counter()
+    try:
+        log_say("Replaying episode", cfg.play_sounds, blocking=True)
+        for idx in range(len(episode_frames)):
+            start_episode_t = time.perf_counter()
 
-        action_array = actions[idx][ACTION]
-        action = {}
-        for i, name in enumerate(dataset.features[ACTION]["names"]):
-            action[name] = action_array[i]
+            action_array = actions[idx][ACTION]
+            action = {}
+            for i, name in enumerate(dataset.features[ACTION]["names"]):
+                action[name] = action_array[i]
 
-        robot_obs = robot.get_observation()
+            robot_obs = robot.get_observation()
 
-        processed_action = robot_action_processor((action, robot_obs))
+            processed_action = robot_action_processor((action, robot_obs))
 
-        _ = robot.send_action(processed_action)
+            _ = robot.send_action(processed_action)
 
-        dt_s = time.perf_counter() - start_episode_t
-        precise_sleep(max(1 / dataset.fps - dt_s, 0.0))
-
-    robot.disconnect()
+            dt_s = time.perf_counter() - start_episode_t
+            precise_sleep(max(1 / dataset.fps - dt_s, 0.0))
+    finally:
+        robot.disconnect()
 
 
 def main():

src/lerobot/scripts/lerobot_setup_can.py

@@ -45,7 +45,7 @@ from dataclasses import dataclass, field
 
 import draccus
 
-from lerobot.utils.import_utils import is_package_available
+from lerobot.utils.import_utils import _can_available
 
 MOTOR_NAMES = {
     0x01: "joint_1",
@@ -336,7 +336,7 @@ def run_speed(cfg: CANSetupConfig):
 
 @draccus.wrap()
 def setup_can(cfg: CANSetupConfig):
-    if not is_package_available("can"):
+    if not _can_available:
         print("Error: python-can not installed. Install with: pip install python-can")
         sys.exit(1)
 

src/lerobot/scripts/lerobot_teleoperate.py

@@ -70,18 +70,22 @@ from lerobot.processor import (
 from lerobot.robots import (  # noqa: F401
     Robot,
     RobotConfig,
+    bi_openarm_follower,
     bi_so_follower,
     earthrover_mini_plus,
     hope_jr,
     koch_follower,
     make_robot_from_config,
     omx_follower,
+    openarm_follower,
     reachy2,
     so_follower,
+    unitree_g1 as unitree_g1_robot,
 )
 from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
+    bi_openarm_leader,
     bi_so_leader,
     gamepad,
     homunculus,
@@ -89,8 +93,10 @@ from lerobot.teleoperators import (  # noqa: F401
     koch_leader,
     make_teleoperator_from_config,
     omx_leader,
+    openarm_leader,
     reachy2_teleoperator,
     so_leader,
+    unitree_g1,
 )
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.robot_utils import precise_sleep

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)
-        # Clip to quantile range then normalize to [-1, 1]
-        clipped = np.clip(data, q01, q99)
-        return 2.0 * (clipped - q01) / denom - 1.0
+        # 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
 
     if mode == NormalizationMode.QUANTILE10:
         q10 = stats.get("q10")
@@ -176,9 +176,8 @@ 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)
-        # Clip to quantile range then normalize to [-1, 1]
-        clipped = np.clip(data, q10, q90)
-        return 2.0 * (clipped - q10) / denom - 1.0
+        # No clipping: match training pipeline NormalizerProcessorStep.
+        return 2.0 * (data - q10) / denom - 1.0
 
     raise ValueError(f"Unsupported normalization mode: {mode}")
 
@@ -306,7 +305,7 @@ 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("physical-intelligence/fast", trust_remote_code=True)
+    base_tokenizer = AutoProcessor.from_pretrained("/fsx/jade_choghari/outputs/libero_tokenizer_wavetoken1", 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))]
@@ -320,6 +319,8 @@ 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/bi_openarm_leader/__init__.py

@@ -0,0 +1,20 @@
+#!/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.
+
+from .bi_openarm_leader import BiOpenArmLeader
+from .config_bi_openarm_leader import BiOpenArmLeaderConfig
+
+__all__ = ["BiOpenArmLeader", "BiOpenArmLeaderConfig"]

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -0,0 +1,135 @@
+#!/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 logging
+from functools import cached_property
+
+from lerobot.processor import RobotAction
+from lerobot.teleoperators.openarm_leader import OpenArmLeaderConfig
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+from ..openarm_leader import OpenArmLeader
+from ..teleoperator import Teleoperator
+from .config_bi_openarm_leader import BiOpenArmLeaderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BiOpenArmLeader(Teleoperator):
+    """
+    Bimanual OpenArm Leader Arms
+    """
+
+    config_class = BiOpenArmLeaderConfig
+    name = "bi_openarm_leader"
+
+    def __init__(self, config: BiOpenArmLeaderConfig):
+        super().__init__(config)
+        self.config = config
+
+        left_arm_config = OpenArmLeaderConfig(
+            id=f"{config.id}_left" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.left_arm_config.port,
+            can_interface=config.left_arm_config.can_interface,
+            use_can_fd=config.left_arm_config.use_can_fd,
+            can_bitrate=config.left_arm_config.can_bitrate,
+            can_data_bitrate=config.left_arm_config.can_data_bitrate,
+            motor_config=config.left_arm_config.motor_config,
+            manual_control=config.left_arm_config.manual_control,
+            position_kd=config.left_arm_config.position_kd,
+            position_kp=config.left_arm_config.position_kp,
+        )
+
+        right_arm_config = OpenArmLeaderConfig(
+            id=f"{config.id}_right" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.right_arm_config.port,
+            can_interface=config.right_arm_config.can_interface,
+            use_can_fd=config.right_arm_config.use_can_fd,
+            can_bitrate=config.right_arm_config.can_bitrate,
+            can_data_bitrate=config.right_arm_config.can_data_bitrate,
+            motor_config=config.right_arm_config.motor_config,
+            manual_control=config.right_arm_config.manual_control,
+            position_kd=config.right_arm_config.position_kd,
+            position_kp=config.right_arm_config.position_kp,
+        )
+
+        self.left_arm = OpenArmLeader(left_arm_config)
+        self.right_arm = OpenArmLeader(right_arm_config)
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        left_arm_features = self.left_arm.action_features
+        right_arm_features = self.right_arm.action_features
+
+        return {
+            **{f"left_{k}": v for k, v in left_arm_features.items()},
+            **{f"right_{k}": v for k, v in right_arm_features.items()},
+        }
+
+    @cached_property
+    def feedback_features(self) -> dict[str, type]:
+        return {}
+
+    @property
+    def is_connected(self) -> bool:
+        return self.left_arm.is_connected and self.right_arm.is_connected
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        self.left_arm.connect(calibrate)
+        self.right_arm.connect(calibrate)
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
+
+    def calibrate(self) -> None:
+        self.left_arm.calibrate()
+        self.right_arm.calibrate()
+
+    def configure(self) -> None:
+        self.left_arm.configure()
+        self.right_arm.configure()
+
+    def setup_motors(self) -> None:
+        raise NotImplementedError(
+            "Motor ID configuration is typically done via manufacturer tools for CAN motors."
+        )
+
+    @check_if_not_connected
+    def get_action(self) -> RobotAction:
+        action_dict = {}
+
+        # Add "left_" prefix
+        left_action = self.left_arm.get_action()
+        action_dict.update({f"left_{key}": value for key, value in left_action.items()})
+
+        # Add "right_" prefix
+        right_action = self.right_arm.get_action()
+        action_dict.update({f"right_{key}": value for key, value in right_action.items()})
+
+        return action_dict
+
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        # TODO: Implement force feedback
+        raise NotImplementedError
+
+    @check_if_not_connected
+    def disconnect(self) -> None:
+        self.left_arm.disconnect()
+        self.right_arm.disconnect()

src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py

@@ -0,0 +1,30 @@
+#!/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.
+
+from dataclasses import dataclass
+
+from lerobot.teleoperators.openarm_leader import OpenArmLeaderConfigBase
+
+from ..config import TeleoperatorConfig
+
+
+@TeleoperatorConfig.register_subclass("bi_openarm_leader")
+@dataclass
+class BiOpenArmLeaderConfig(TeleoperatorConfig):
+    """Configuration class for Bi OpenArm Follower robots."""
+
+    left_arm_config: OpenArmLeaderConfigBase
+    right_arm_config: OpenArmLeaderConfigBase

src/lerobot/teleoperators/bi_so_leader/bi_so_leader.py

@@ -18,7 +18,7 @@ import logging
 from functools import cached_property
 
 from lerobot.teleoperators.so_leader import SOLeaderTeleopConfig
-from lerobot.utils.decorators import check_if_not_connected
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..so_leader import SOLeader
 from ..teleoperator import Teleoperator
@@ -72,6 +72,7 @@ class BiSOLeader(Teleoperator):
     def is_connected(self) -> bool:
         return self.left_arm.is_connected and self.right_arm.is_connected
 
+    @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
         self.left_arm.connect(calibrate)
         self.right_arm.connect(calibrate)
@@ -110,6 +111,7 @@ class BiSOLeader(Teleoperator):
         # TODO: Implement force feedback
         raise NotImplementedError
 
+    @check_if_not_connected
     def disconnect(self) -> None:
         self.left_arm.disconnect()
         self.right_arm.disconnect()

src/lerobot/teleoperators/openarm_leader/__init__.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config_openarm_leader import OpenArmLeaderConfig, OpenArmLeaderConfigBase
+from .openarm_leader import OpenArmLeader
+
+__all__ = ["OpenArmLeader", "OpenArmLeaderConfig", "OpenArmLeaderConfigBase"]

src/lerobot/teleoperators/openarm_leader/config_openarm_leader.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass, field
+
+from ..config import TeleoperatorConfig
+
+
+@dataclass
+class OpenArmLeaderConfigBase:
+    """Base configuration for the OpenArms leader/teleoperator with Damiao motors."""
+
+    # CAN interfaces - one per arm
+    # Arm CAN interface (e.g., "can3")
+    # Linux: "can0", "can1", etc.
+    port: str
+
+    # CAN interface type: "socketcan" (Linux), "slcan" (serial), or "auto" (auto-detect)
+    can_interface: str = "socketcan"
+
+    # CAN FD settings (OpenArms uses CAN FD by default)
+    use_can_fd: bool = True
+    can_bitrate: int = 1000000  # Nominal bitrate (1 Mbps)
+    can_data_bitrate: int = 5000000  # Data bitrate for CAN FD (5 Mbps)
+
+    # Motor configuration for OpenArms (7 DOF per arm)
+    # Maps motor names to (send_can_id, recv_can_id, motor_type)
+    # Based on: https://docs.openarm.dev/software/setup/configure-test
+    # OpenArms uses 4 types of motors:
+    # - DM8009 (DM-J8009P-2EC) for shoulders (high torque)
+    # - DM4340P and DM4340 for shoulder rotation and elbow
+    # - DM4310 (DM-J4310-2EC V1.1) for wrist and gripper
+    motor_config: dict[str, tuple[int, int, str]] = field(
+        default_factory=lambda: {
+            "joint_1": (0x01, 0x11, "dm8009"),  # J1 - Shoulder pan (DM8009)
+            "joint_2": (0x02, 0x12, "dm8009"),  # J2 - Shoulder lift (DM8009)
+            "joint_3": (0x03, 0x13, "dm4340"),  # J3 - Shoulder rotation (DM4340)
+            "joint_4": (0x04, 0x14, "dm4340"),  # J4 - Elbow flex (DM4340)
+            "joint_5": (0x05, 0x15, "dm4310"),  # J5 - Wrist roll (DM4310)
+            "joint_6": (0x06, 0x16, "dm4310"),  # J6 - Wrist pitch (DM4310)
+            "joint_7": (0x07, 0x17, "dm4310"),  # J7 - Wrist rotation (DM4310)
+            "gripper": (0x08, 0x18, "dm4310"),  # J8 - Gripper (DM4310)
+        }
+    )
+
+    # Torque mode settings for manual control
+    # When enabled, motors have torque disabled for manual movement
+    manual_control: bool = True
+
+    # TODO(Steven, Pepijn): Not used ... ?
+    # MIT control parameters (used when manual_control=False for torque control)
+    # List of 8 values: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper]
+    position_kp: list[float] = field(
+        default_factory=lambda: [240.0, 240.0, 240.0, 240.0, 24.0, 31.0, 25.0, 16.0]
+    )
+    position_kd: list[float] = field(default_factory=lambda: [3.0, 3.0, 3.0, 3.0, 0.2, 0.2, 0.2, 0.2])
+
+
+@TeleoperatorConfig.register_subclass("openarm_leader")
+@dataclass
+class OpenArmLeaderConfig(TeleoperatorConfig, OpenArmLeaderConfigBase):
+    pass

src/lerobot/teleoperators/openarm_leader/openarm_leader.py

@@ -0,0 +1,222 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import time
+from typing import Any
+
+from lerobot.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.motors.damiao import DamiaoMotorsBus
+from lerobot.processor import RobotAction
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+from ..teleoperator import Teleoperator
+from .config_openarm_leader import OpenArmLeaderConfig
+
+logger = logging.getLogger(__name__)
+
+
+class OpenArmLeader(Teleoperator):
+    """
+    OpenArm Leader/Teleoperator Arm with Damiao motors.
+
+    This teleoperator uses CAN bus communication to read positions from
+    Damiao motors that are manually moved (torque disabled).
+    """
+
+    config_class = OpenArmLeaderConfig
+    name = "openarm_leader"
+
+    def __init__(self, config: OpenArmLeaderConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Arm motors
+        motors: dict[str, Motor] = {}
+        for motor_name, (send_id, recv_id, motor_type_str) in config.motor_config.items():
+            motor = Motor(
+                send_id, motor_type_str, MotorNormMode.DEGREES
+            )  # Always use degrees for Damiao motors
+            motor.recv_id = recv_id
+            motor.motor_type_str = motor_type_str
+            motors[motor_name] = motor
+
+        self.bus = DamiaoMotorsBus(
+            port=self.config.port,
+            motors=motors,
+            calibration=self.calibration,
+            can_interface=self.config.can_interface,
+            use_can_fd=self.config.use_can_fd,
+            bitrate=self.config.can_bitrate,
+            data_bitrate=self.config.can_data_bitrate if self.config.use_can_fd else None,
+        )
+
+    @property
+    def action_features(self) -> dict[str, type]:
+        """Features produced by this teleoperator."""
+        features: dict[str, type] = {}
+        for motor in self.bus.motors:
+            features[f"{motor}.pos"] = float
+            features[f"{motor}.vel"] = float
+            features[f"{motor}.torque"] = float
+        return features
+
+    @property
+    def feedback_features(self) -> dict[str, type]:
+        """Feedback features (not implemented for OpenArms)."""
+        return {}
+
+    @property
+    def is_connected(self) -> bool:
+        """Check if teleoperator is connected."""
+        return self.bus.is_connected
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        Connect to the teleoperator.
+
+        For manual control, we disable torque after connecting so the
+        arm can be moved by hand.
+        """
+
+        # Connect to CAN bus
+        logger.info(f"Connecting arm on {self.config.port}...")
+        self.bus.connect()
+
+        # Run calibration if needed
+        if not self.is_calibrated and calibrate:
+            logger.info(
+                "Mismatch between calibration values in the motor and the calibration file or no calibration file found"
+            )
+            self.calibrate()
+
+        self.configure()
+
+        if self.is_calibrated:
+            self.bus.set_zero_position()
+
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        """Check if teleoperator is calibrated."""
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        """
+        Run calibration procedure for OpenArms leader.
+
+        The calibration procedure:
+        1. Disable torque (if not already disabled)
+        2. Ask user to position arm in zero position (hanging with gripper closed)
+        3. Set this as zero position
+        4. Record range of motion for each joint
+        5. Save calibration
+        """
+        if self.calibration:
+            # Calibration file exists, ask user whether to use it or run new calibration
+            user_input = input(
+                f"Press ENTER to use provided calibration file associated with the id {self.id}, or type 'c' and press ENTER to run calibration: "
+            )
+            if user_input.strip().lower() != "c":
+                logger.info(f"Writing calibration file associated with the id {self.id} to the motors")
+                self.bus.write_calibration(self.calibration)
+                return
+
+        logger.info(f"\nRunning calibration for {self}")
+        self.bus.disable_torque()
+
+        # Step 1: Set zero position
+        input(
+            "\nCalibration: Set Zero Position)\n"
+            "Position the arm in the following configuration:\n"
+            "  - Arm hanging straight down\n"
+            "  - Gripper closed\n"
+            "Press ENTER when ready..."
+        )
+
+        # Set current position as zero for all motors
+        self.bus.set_zero_position()
+        logger.info("Arm zero position set.")
+
+        logger.info("Setting range: -90° to +90° by default for all joints")
+        # TODO(Steven, Pepijn): Check if MotorCalibration is actually needed here given that we only use Degrees
+        for motor_name, motor in self.bus.motors.items():
+            self.calibration[motor_name] = MotorCalibration(
+                id=motor.id,
+                drive_mode=0,
+                homing_offset=0,
+                range_min=-90,
+                range_max=90,
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print(f"Calibration saved to {self.calibration_fpath}")
+
+    def configure(self) -> None:
+        """
+        Configure motors for manual teleoperation.
+
+        For manual control, we disable torque so the arm can be moved by hand.
+        """
+
+        return self.bus.disable_torque() if self.config.manual_control else self.bus.configure_motors()
+
+    def setup_motors(self) -> None:
+        raise NotImplementedError(
+            "Motor ID configuration is typically done via manufacturer tools for CAN motors."
+        )
+
+    @check_if_not_connected
+    def get_action(self) -> RobotAction:
+        """
+        Get current action from the leader arm.
+
+        This is the main method for teleoperators - it reads the current state
+        of the leader arm and returns it as an action that can be sent to a follower.
+
+        Reads all motor states (pos/vel/torque) in one CAN refresh cycle.
+        """
+        start = time.perf_counter()
+
+        action_dict: dict[str, Any] = {}
+
+        # Use sync_read_all_states to get pos/vel/torque in one go
+        states = self.bus.sync_read_all_states()
+        for motor in self.bus.motors:
+            state = states.get(motor, {})
+            action_dict[f"{motor}.pos"] = state.get("position")
+            action_dict[f"{motor}.vel"] = state.get("velocity")
+            action_dict[f"{motor}.torque"] = state.get("torque")
+
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        return action_dict
+
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        raise NotImplementedError("Feedback is not yet implemented for OpenArm leader.")
+
+    @check_if_not_connected
+    def disconnect(self) -> None:
+        """Disconnect from teleoperator."""
+
+        # Disconnect CAN bus
+        # For manual control, ensure torque is disabled before disconnecting
+        self.bus.disconnect(disable_torque=self.config.manual_control)
+        logger.info(f"{self} disconnected.")

src/lerobot/teleoperators/unitree_g1/__init__.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .config_unitree_g1 import ExoskeletonArmPortConfig, UnitreeG1TeleoperatorConfig
+from .exo_calib import ExoskeletonCalibration, ExoskeletonJointCalibration
+from .exo_ik import ExoskeletonIKHelper
+from .exo_serial import ExoskeletonArm
+from .unitree_g1 import UnitreeG1Teleoperator

src/lerobot/teleoperators/unitree_g1/config_unitree_g1.py

@@ -0,0 +1,37 @@
+#!/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.
+
+from dataclasses import dataclass, field
+
+from ..config import TeleoperatorConfig
+
+
+@dataclass
+class ExoskeletonArmPortConfig:
+    """Serial port configuration for individual exoskeleton arm."""
+
+    port: str = ""
+    baud_rate: int = 115200
+
+
+@TeleoperatorConfig.register_subclass("unitree_g1")
+@dataclass
+class UnitreeG1TeleoperatorConfig(TeleoperatorConfig):
+    left_arm_config: ExoskeletonArmPortConfig = field(default_factory=ExoskeletonArmPortConfig)
+    right_arm_config: ExoskeletonArmPortConfig = field(default_factory=ExoskeletonArmPortConfig)
+
+    # Frozen joints (comma-separated joint names that won't be moved by IK)
+    frozen_joints: str = ""

src/lerobot/teleoperators/unitree_g1/exo_calib.py

@@ -0,0 +1,446 @@
+#!/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.
+
+"""
+This module handles calibration of hall effect sensors used in the exoskeleton.
+Each joint has a pair of ADC channels outputting sin and cos values that trace an ellipse
+as the joint rotates due to imprecision in magnet/sensor placement. We fit this ellipse to a unit circle,
+and calculate arctan2 of the unit circle to get the joint angle.
+We then store the ellipse parameters and the zero offset for each joint to be used at runtime.
+"""
+
+import json
+import logging
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from pathlib import Path
+
+import numpy as np
+import serial
+
+logger = logging.getLogger(__name__)
+
+
+# exoskeleton joint names -> ADC channel pairs. TODO: add wrist pitch and wrist yaw
+JOINTS = {
+    "shoulder_pitch": (0, 1),
+    "shoulder_yaw": (2, 3),
+    "shoulder_roll": (4, 5),
+    "elbow_flex": (6, 7),
+    "wrist_roll": (14, 15),
+}
+
+
+@dataclass
+class ExoskeletonJointCalibration:
+    name: str  # joint name
+    center_fit: list[float]  # center of the ellipse
+    T: list[list[float]]  # 2x2 transformation matrix
+    zero_offset: float = 0.0  # angle at neutral pose
+
+
+@dataclass
+class ExoskeletonCalibration:
+    """Full calibration data for an exoskeleton arm."""
+
+    version: int = 2
+    side: str = ""
+    adc_max: int = 2**12 - 1
+    joints: list[ExoskeletonJointCalibration] = field(default_factory=list)
+
+    def to_dict(self) -> dict:
+        return {
+            "version": self.version,
+            "side": self.side,
+            "adc_max": self.adc_max,
+            "joints": [
+                {
+                    "name": j.name,
+                    "center_fit": j.center_fit,
+                    "T": j.T,
+                    "zero_offset": j.zero_offset,
+                }
+                for j in self.joints
+            ],
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "ExoskeletonCalibration":
+        joints = [
+            ExoskeletonJointCalibration(
+                name=j["name"],
+                center_fit=j["center_fit"],
+                T=j["T"],
+                zero_offset=j.get("zero_offset", 0.0),
+            )
+            for j in data.get("joints", [])
+        ]
+        return cls(
+            version=data.get("version", 2),
+            side=data.get("side", ""),
+            adc_max=data.get("adc_max", 2**12 - 1),
+            joints=joints,
+        )
+
+
+@dataclass(frozen=True)
+class CalibParams:
+    fit_every: float = 0.15
+    min_fit_points: int = 60
+    fit_window: int = 900
+    max_fit_points: int = 300
+    trim_low: float = 0.05
+    trim_high: float = 0.95
+    median_window: int = 5
+    history: int = 3500
+    draw_hz: float = 120.0
+    sample_count: int = 50
+
+
+def normalize_angle(angle: float) -> float:
+    while angle > np.pi:
+        angle -= 2 * np.pi
+    while angle < -np.pi:
+        angle += 2 * np.pi
+    return angle
+
+
+def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple[np.ndarray, float]:
+    """
+    Applies calibration to each joint: raw → centered → ellipse-to-circle → angle.
+    """
+    pair = JOINTS[j.name]
+    s, c = raw16[pair[0]], raw16[pair[1]]  # get sin and cos
+    p = np.array([float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2])  # center the raw values
+    z = np.asarray(j.T) @ (
+        p - np.asarray(j.center_fit)
+    )  # center the ellipse and invert the transformation matrix to get unit circle coords
+    ang = float(np.arctan2(z[1], z[0])) - j.zero_offset  # calculate the anvgle and apply the zero offset
+    return z, normalize_angle(-ang)  # ensure range is [-pi, pi]
+
+
+def exo_raw_to_angles(raw16: list[int], calib: ExoskeletonCalibration) -> dict[str, float]:
+    """Convert raw sensor readings to joint angles using calibration."""
+    return {j.name: joint_z_and_angle(raw16, j)[1] for j in calib.joints}
+
+
+def run_exo_calibration(
+    ser: serial.Serial,
+    side: str,
+    save_path: Path,
+    params: CalibParams | None = None,
+) -> ExoskeletonCalibration:
+    """
+    Run interactive calibration for an exoskeleton arm.
+    """
+    try:
+        import cv2
+        import matplotlib.pyplot as plt
+    except ImportError as e:
+        raise ImportError(
+            "Calibration requires matplotlib and opencv-python. "
+            "Install with: pip install matplotlib opencv-python"
+        ) from e
+
+    from .exo_serial import read_raw_from_serial
+
+    params = params or CalibParams()
+    joint_list = list(JOINTS.items())  # Convert dict to list for indexing
+    logger.info(f"Starting calibration for {side} exoskeleton arm")
+
+    def running_median(win: deque) -> float:
+        return float(np.median(np.fromiter(win, dtype=float)))
+
+    def read_joint_point(raw16: list[int], pair: tuple[int, int]):
+        s, c = raw16[pair[0]], raw16[pair[1]]
+        return float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2, float(s), float(c)
+
+    def select_fit_subset(xs, ys):
+        """Select and filter points for ellipse fitting. Trims outliers by radius and downsamples."""
+        n = min(params.fit_window, len(xs))
+        if n <= 0:
+            return None, None
+        x = np.asarray(list(xs)[-n:], dtype=float)  # most recent n samples
+        y = np.asarray(list(ys)[-n:], dtype=float)
+        r = np.sqrt(x * x + y * y)  # radius from origin
+        if len(r) >= 20:
+            lo, hi = np.quantile(r, params.trim_low), np.quantile(r, params.trim_high)  # outlier bounds
+            keep = (r >= lo) & (r <= hi)
+            x, y = x[keep], y[keep]  # remove outliers
+        if len(x) > params.max_fit_points:
+            idx = np.linspace(0, len(x) - 1, params.max_fit_points).astype(int)  # downsample evenly
+            x, y = x[idx], y[idx]
+        return x, y
+
+    def fit_ellipse_opencv(x, y):
+        """Fit ellipse to (x,y) points using OpenCV. Returns center, axes, rotation matrix, and outline."""
+        x, y = np.asarray(x, dtype=float), np.asarray(y, dtype=float)
+        if len(x) < 5:
+            return None
+        pts = np.stack([x, y], axis=1).astype(np.float32).reshape(-1, 1, 2)
+        try:
+            (xc, yc), (w, h), angle_deg = cv2.fitEllipse(pts)  # returns center, axes, rotation in degrees
+        except cv2.error:
+            return None
+        a, b = float(w) * 0.5, float(h) * 0.5  # get ellipse major and minor semi-axes
+        phi = np.deg2rad(float(angle_deg))  # to rad
+        if b > a:  # ensure major axis is a
+            a, b = b, a
+            phi += np.pi / 2.0
+        if not np.isfinite(a) or not np.isfinite(b) or a <= 1e-6 or b <= 1e-6:
+            return None
+        cp, sp = float(np.cos(phi)), float(np.sin(phi))  #
+        rot = np.array([[cp, -sp], [sp, cp]], dtype=float)  # 2x2 rotation matrix
+        center = np.array([float(xc), float(yc)], dtype=float)  # offset vector
+        tt = np.linspace(0, 2 * np.pi, 360)
+        outline = (rot @ np.stack([a * np.cos(tt), b * np.sin(tt)])).T + center  # for viz
+        return {"center": center, "a": a, "b": b, "R": rot, "ex": outline[:, 0], "ey": outline[:, 1]}
+
+    # Setup matplotlib
+    plt.ion()
+    fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6))
+    ax0.set_xlabel("cos - center")
+    ax0.set_ylabel("sin - center")
+    ax0.grid(True, alpha=0.25)
+    ax0.set_aspect("equal", adjustable="box")
+    ax1.set_title("Unit circle + angle")
+    ax1.set_xlabel("x")
+    ax1.set_ylabel("y")
+    ax1.grid(True, alpha=0.25)
+    ax1.set_aspect("equal", adjustable="box")
+    tt = np.linspace(0, 2 * np.pi, 360)
+    ax1.plot(np.cos(tt), np.sin(tt), "k-", linewidth=1)
+    ax0.set_xlim(-2200, 2200)
+    ax0.set_ylim(-2200, 2200)
+    ax1.set_xlim(-1.4, 1.4)
+    ax1.set_ylim(-1.4, 1.4)
+
+    sc0 = ax0.scatter([], [], s=6, animated=True)
+    (ell_line,) = ax0.plot([], [], "r-", linewidth=2, animated=True)
+    sc1 = ax1.scatter([], [], s=6, animated=True)
+    (radius_line,) = ax1.plot([], [], "g-", linewidth=2, animated=True)
+    angle_text = ax1.text(
+        0.02, 0.98, "", transform=ax1.transAxes, va="top", ha="left", fontsize=12, animated=True
+    )
+
+    fig.canvas.draw()
+    bg0 = fig.canvas.copy_from_bbox(ax0.bbox)
+    bg1 = fig.canvas.copy_from_bbox(ax1.bbox)
+
+    # State
+    joints_out = []
+    joint_idx = 0
+    phase = "ellipse"
+    advance_requested = False
+    zero_samples = []
+
+    def on_key(event):
+        nonlocal advance_requested
+        if event.key in ("n", "N", "enter", " "):
+            advance_requested = True
+
+    fig.canvas.mpl_connect("key_press_event", on_key)
+
+    def reset_state():
+        return {
+            "xs": deque(maxlen=params.history),
+            "ys": deque(maxlen=params.history),
+            "xu": deque(maxlen=params.history),
+            "yu": deque(maxlen=params.history),
+            "win_s": deque(maxlen=params.median_window),
+            "win_c": deque(maxlen=params.median_window),
+            "ellipse_cache": None,
+            "T": None,
+            "center_fit": None,
+            "have_transform": False,
+            "latest_z": None,
+            "last_fit": 0.0,
+        }
+
+    state = reset_state()
+    last_draw = 0.0
+    name, pair = joint_list[joint_idx]
+    fig.canvas.manager.set_window_title(f"[{joint_idx + 1}/{len(joint_list)}] {name} - ELLIPSE")
+    ax0.set_title(f"{name} raw (filtered)")
+    logger.info(f"[{joint_idx + 1}/{len(joint_list)}] Calibrating {name}")
+    logger.info("Step 1: Move joint around to map ellipse, then press 'n'")
+
+    try:
+        while plt.fignum_exists(fig.number):
+            name, pair = joint_list[joint_idx]
+
+            # Handles calibration GUI state: ellipse → zero_pose → next joint -> ellipse -> ...
+            if phase == "ellipse" and advance_requested and state["have_transform"]:
+                joints_out.append(
+                    {
+                        "name": name,
+                        "center_fit": state["center_fit"].tolist(),
+                        "T": state["T"].tolist(),
+                    }
+                )
+                logger.info(f"  -> Ellipse saved for {name}")
+                phase, zero_samples, advance_requested = "zero_pose", [], False
+                fig.canvas.manager.set_window_title(f"[{joint_idx + 1}/{len(joint_list)}] {name} - ZERO POSE")
+                ax0.set_title(f"{name} - hold zero pose")
+                fig.canvas.draw()
+                bg0, bg1 = fig.canvas.copy_from_bbox(ax0.bbox), fig.canvas.copy_from_bbox(ax1.bbox)
+                logger.info(f"Step 2: Hold {name} in zero position, then press 'n'")
+
+            elif phase == "ellipse" and advance_requested and not state["have_transform"]:
+                logger.info("  (Need valid fit first - keep moving the joint)")
+                advance_requested = False
+
+            elif phase == "zero_pose" and advance_requested:
+                if len(zero_samples) >= params.sample_count:
+                    zero_offset = float(np.mean(zero_samples[-params.sample_count :]))
+                    joints_out[-1]["zero_offset"] = zero_offset
+                    logger.info(f"  -> {name} zero: {zero_offset:+.3f} rad ({np.degrees(zero_offset):+.1f}°)")
+                    joint_idx += 1
+                    advance_requested = False
+
+                    if joint_idx >= len(joint_list):
+                        # All joints done
+                        calib = ExoskeletonCalibration(
+                            version=2,
+                            side=side,
+                            adc_max=2**12 - 1,
+                            joints=[
+                                ExoskeletonJointCalibration(
+                                    name=j["name"],
+                                    center_fit=j["center_fit"],
+                                    T=j["T"],
+                                    zero_offset=j.get("zero_offset", 0.0),
+                                )
+                                for j in joints_out
+                            ],
+                        )
+                        save_path.parent.mkdir(parents=True, exist_ok=True)
+                        with open(save_path, "w") as f:
+                            json.dump(calib.to_dict(), f, indent=2)
+                        logger.info(f"Saved calibration to {save_path}")
+                        logger.info("Calibration complete!")
+                        plt.close(fig)
+                        return calib
+
+                    # Next joint
+                    phase, state = "ellipse", reset_state()
+                    name, pair = joint_list[joint_idx]
+                    fig.canvas.manager.set_window_title(
+                        f"[{joint_idx + 1}/{len(joint_list)}] {name} - ELLIPSE"
+                    )
+                    ax0.set_title(f"{name} raw (filtered)")
+                    fig.canvas.draw()
+                    bg0, bg1 = fig.canvas.copy_from_bbox(ax0.bbox), fig.canvas.copy_from_bbox(ax1.bbox)
+                    logger.info(f"[{joint_idx + 1}/{len(joint_list)}] Calibrating {name}")
+                    logger.info("Step 1: Move joint around to map ellipse, then press 'n'")
+                else:
+                    logger.info(
+                        f"  (Collecting samples: {len(zero_samples)}/{params.sample_count} - hold still)"
+                    )
+                    advance_requested = False
+
+            # Read sensor
+            raw16 = read_raw_from_serial(ser)
+            if raw16 is not None:
+                x_raw, y_raw, s_raw, c_raw = read_joint_point(raw16, pair)
+
+                if phase == "ellipse":
+                    if state["have_transform"]:
+                        z = state["T"] @ (np.array([x_raw, y_raw]) - state["center_fit"])
+                        state["xu"].append(float(z[0]))
+                        state["yu"].append(float(z[1]))
+                        state["latest_z"] = (float(z[0]), float(z[1]))
+                    state["win_s"].append(s_raw)
+                    state["win_c"].append(c_raw)
+                    if len(state["win_s"]) >= max(3, params.median_window):
+                        state["ys"].append(running_median(state["win_s"]) - (2**12 - 1) / 2)
+                        state["xs"].append(running_median(state["win_c"]) - (2**12 - 1) / 2)
+                else:
+                    jdata = joints_out[-1]
+                    z = np.array(jdata["T"]) @ (np.array([x_raw, y_raw]) - np.array(jdata["center_fit"]))
+                    zero_samples.append(float(np.arctan2(z[1], z[0])))
+                    state["latest_z"] = (float(z[0]), float(z[1]))
+
+            # Ellipse fitting
+            t = time.time()
+            if (
+                phase == "ellipse"
+                and (t - state["last_fit"]) >= params.fit_every
+                and len(state["xs"]) >= params.min_fit_points
+            ):
+                xfit, yfit = select_fit_subset(state["xs"], state["ys"])
+                if xfit is not None and len(xfit) >= params.min_fit_points:
+                    fit = fit_ellipse_opencv(xfit, yfit)
+                    if fit is not None:
+                        state["center_fit"] = fit["center"]
+                        state["T"] = np.diag([1.0 / fit["a"], 1.0 / fit["b"]]) @ fit["R"].T
+                        state["ellipse_cache"] = (fit["ex"], fit["ey"])
+                        state["have_transform"] = True
+                state["last_fit"] = t
+
+            # Drawing
+            if (t - last_draw) >= 1.0 / params.draw_hz:
+                fig.canvas.restore_region(bg0)
+                fig.canvas.restore_region(bg1)
+
+                if phase == "ellipse":
+                    sc0.set_offsets(np.c_[state["xs"], state["ys"]] if state["xs"] else np.empty((0, 2)))
+                    ax0.draw_artist(sc0)
+                    ell_line.set_data(*state["ellipse_cache"] if state["ellipse_cache"] else ([], []))
+                    ax0.draw_artist(ell_line)
+                    sc1.set_offsets(np.c_[state["xu"], state["yu"]] if state["xu"] else np.empty((0, 2)))
+                    ax1.draw_artist(sc1)
+                    if state["latest_z"]:
+                        zx, zy = state["latest_z"]
+                        radius_line.set_data([0.0, zx], [0.0, zy])
+                        ang = float(np.arctan2(zy, zx))
+                        angle_text.set_text(
+                            f"angle: {ang:+.3f} rad  ({np.degrees(ang):+.1f}°)\nmove {name}, press 'n' to advance"
+                        )
+                    else:
+                        radius_line.set_data([], [])
+                        angle_text.set_text("(waiting for fit)")
+                else:
+                    sc0.set_offsets(np.empty((0, 2)))
+                    ax0.draw_artist(sc0)
+                    ell_line.set_data([], [])
+                    ax0.draw_artist(ell_line)
+                    if state["latest_z"]:
+                        zx, zy = state["latest_z"]
+                        sc1.set_offsets([[zx, zy]])
+                        radius_line.set_data([0.0, zx], [0.0, zy])
+                        ang = float(np.arctan2(zy, zx))
+                        angle_text.set_text(
+                            f"Zero pose for {name}\nangle: {ang:+.3f} rad\nsamples: {len(zero_samples)}/{params.sample_count}\nhold still, press 'n'"
+                        )
+                    else:
+                        sc1.set_offsets(np.empty((0, 2)))
+                        radius_line.set_data([], [])
+                        angle_text.set_text("(waiting for data)")
+                    ax1.draw_artist(sc1)
+
+                ax1.draw_artist(radius_line)
+                ax1.draw_artist(angle_text)
+                fig.canvas.blit(ax0.bbox)
+                fig.canvas.blit(ax1.bbox)
+                fig.canvas.flush_events()
+                last_draw = t
+
+            plt.pause(0.001)
+
+    finally:
+        plt.close(fig)

src/lerobot/teleoperators/unitree_g1/exo_ik.py

@@ -0,0 +1,353 @@
+#!/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.
+
+"""
+IK helper for exoskeleton-to-G1 teleoperation. We map Exoskeleton joint angles to end-effector pose in world frame,
+visualizing the result in meshcat after calibration.
+"""
+
+import logging
+import os
+from dataclasses import dataclass
+
+import numpy as np
+
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex
+from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK
+
+from .exo_calib import JOINTS
+
+logger = logging.getLogger(__name__)
+
+
+def _frame_id(model, name: str) -> int | None:
+    try:
+        fid = model.getFrameId(name)
+        return fid if 0 <= fid < model.nframes else None
+    except Exception:
+        return None
+
+
+@dataclass
+class ArmCfg:
+    side: str  # "left" | "right"
+    urdf: str  # exo_left.urdf / exo_right.urdf
+    root: str  # "exo_left" / "exo_right"
+    g1_ee: str  # "l_ee" / "r_ee"
+    offset: np.ndarray  # world offset for viz + target
+    marker_prefix: str  # "left" / "right"
+
+
+class Markers:
+    """Creates meshcat visualization primitives, showing end-effector frames of exoskeleton and G1"""
+
+    def __init__(self, viewer):
+        self.v = viewer
+
+    def sphere(self, path: str, r: float, rgba: tuple[float, float, float, float]):
+        import meshcat.geometry as mg
+
+        c = (int(rgba[0] * 255) << 16) | (int(rgba[1] * 255) << 8) | int(rgba[2] * 255)
+        self.v[path].set_object(
+            mg.Sphere(r),
+            mg.MeshPhongMaterial(color=c, opacity=rgba[3], transparent=rgba[3] < 1.0),
+        )
+
+    def axes(self, path: str, axis_len: float = 0.1, axis_w: int = 6):
+        import meshcat.geometry as mg
+
+        pts = np.array(
+            [[0, 0, 0], [axis_len, 0, 0], [0, 0, 0], [0, axis_len, 0], [0, 0, 0], [0, 0, axis_len]],
+            dtype=np.float32,
+        ).T
+        cols = np.array(
+            [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]],
+            dtype=np.float32,
+        ).T
+        self.v[path].set_object(
+            mg.LineSegments(
+                mg.PointsGeometry(position=pts, color=cols),
+                mg.LineBasicMaterial(linewidth=axis_w, vertexColors=True),
+            )
+        )
+
+    def tf(self, path: str, mat: np.ndarray):
+        self.v[path].set_transform(mat)
+
+
+class ExoskeletonIKHelper:
+    """
+    - Loads G1 robot and exoskeleton URDF models via Pinocchio
+    - Computes forward kinematics on exoskeleton to get end-effector poses
+    - Solves inverse kinematics on G1 to match those poses
+    - Provides meshcat visualization showing both robots and targets
+
+    Args:
+        frozen_joints: List of G1 joint names to exclude from IK (kept at neutral).
+    """
+
+    def __init__(self, frozen_joints: list[str] | None = None):
+        try:
+            import pinocchio as pin
+        except ImportError as e:
+            raise ImportError("ik mode needs pinocchio: pip install pin") from e
+
+        self.pin = pin
+        self.frozen_joints = frozen_joints or []
+
+        self.g1_ik = G1_29_ArmIK()
+        self.robot_g1 = self.g1_ik.reduced_robot
+        self.robot_g1.data = self.robot_g1.model.createData()
+        self.q_g1 = pin.neutral(self.robot_g1.model)
+
+        assets_dir = os.path.join(self.g1_ik.repo_path, "assets")
+
+        self.frozen_idx = self._frozen_joint_indices()
+
+        self.arms = [
+            ArmCfg(
+                side="left",
+                urdf=os.path.join(assets_dir, "exo_left.urdf"),
+                root="exo_left",
+                g1_ee="L_ee",
+                offset=np.array([0.6, 0.3, 0.0]),
+                marker_prefix="left",
+            ),
+            ArmCfg(
+                side="right",
+                urdf=os.path.join(assets_dir, "exo_right.urdf"),
+                root="exo_right",
+                g1_ee="R_ee",
+                offset=np.array([0.6, -0.3, 0.0]),
+                marker_prefix="right",
+            ),
+        ]
+
+        self.exo = {}  # side -> pin.RobotWrapper
+        self.q_exo = {}  # side -> q
+        self.ee_id_exo = {}  # side -> frame id
+        self.qmap = {}  # side -> {joint_name: q_idx}
+        self.ee_id_g1 = {}  # side -> frame id
+
+        self._load_exo_models(assets_dir)
+        for a in self.arms:
+            self.ee_id_g1[a.side] = _frame_id(self.robot_g1.model, a.g1_ee)
+
+        self.viewer = None
+        self.markers: Markers | None = None
+        self.viz_g1 = None
+        self.viz_exo = {}  # side -> viz
+
+    def _frozen_joint_indices(self) -> dict[str, int]:
+        out = {}
+        m = self.robot_g1.model
+        for name in self.frozen_joints:
+            if name in m.names:
+                jid = m.getJointId(name)
+                out[name] = m.idx_qs[jid]
+                logger.info(f"freezing joint: {name} (q_idx={out[name]})")
+        return out
+
+    def _find_exo_ee(self, model, ee_name: str = "ee") -> int:
+        ee = _frame_id(model, ee_name)
+        if ee is not None:
+            return ee
+        for fid in reversed(range(model.nframes)):
+            if model.frames[fid].type == self.pin.FrameType.BODY:
+                return fid
+        return 0
+
+    def _build_joint_map(self, robot) -> dict[str, int]:
+        m = robot.model
+        return {n: m.idx_qs[m.getJointId(n)] for n in JOINTS if n in m.names}
+
+    def _load_exo_models(self, assets_dir: str):
+        pin = self.pin
+        for a in self.arms:
+            if not os.path.exists(a.urdf):
+                logger.warning(f"{a.side} exo urdf not found: {a.urdf}")
+                continue
+            r = pin.RobotWrapper.BuildFromURDF(a.urdf, assets_dir)
+            self.exo[a.side] = r
+            self.q_exo[a.side] = pin.neutral(r.model)
+            self.ee_id_exo[a.side] = self._find_exo_ee(r.model)
+            self.qmap[a.side] = self._build_joint_map(r)
+            logger.info(f"loaded {a.side} exo urdf: {a.urdf}")
+
+    def init_visualization(self):
+        """
+        Creates a browser-based visualization of exoskeleton and G1 robot,
+        highlighting end-effector frames and target positions.
+        """
+        try:
+            from pinocchio.visualize import MeshcatVisualizer
+        except ImportError as e:
+            logger.warning(f"meshcat viz unavailable: {e}")
+            return
+
+        # g1
+        self.viz_g1 = MeshcatVisualizer(
+            self.robot_g1.model, self.robot_g1.collision_model, self.robot_g1.visual_model
+        )
+        self.viz_g1.initViewer(open=True)
+        self.viz_g1.loadViewerModel("g1")
+        self.viz_g1.display(self.q_g1)
+
+        self.viewer = self.viz_g1.viewer
+        self.markers = Markers(self.viewer)
+
+        # exos
+        for a in self.arms:
+            if a.side not in self.exo:
+                continue
+            r = self.exo[a.side]
+            v = MeshcatVisualizer(r.model, r.collision_model, r.visual_model)
+            v.initViewer(open=False)
+            v.viewer = self.viewer
+            v.loadViewerModel(a.root)
+            offset_tf = np.eye(4)
+            offset_tf[:3, 3] = a.offset
+            self.viewer[a.root].set_transform(offset_tf)
+            v.display(self.q_exo[a.side])
+            self.viz_exo[a.side] = v
+
+        # markers
+        for a in self.arms:
+            p = a.marker_prefix
+            self.markers.sphere(f"markers/{p}_exo_ee", 0.012, (0.2, 1.0, 0.2, 0.9))
+            self.markers.sphere(f"markers/{p}_g1_ee", 0.015, (1.0, 0.2, 0.2, 0.9))
+            self.markers.sphere(f"markers/{p}_ik_target", 0.015, (0.1, 0.3, 1.0, 0.9))
+            self.markers.axes(f"markers/{p}_exo_axes", 0.06)
+            self.markers.axes(f"markers/{p}_g1_axes", 0.08)
+
+        logger.info(f"meshcat viz initialized: {self.viewer.url()}")
+        print(f"\nmeshcat url: {self.viewer.url()}\n")
+
+    def _fk_target_world(self, side: str, angles: dict[str, float]) -> np.ndarray | None:
+        """returns wrist frame target to be used for G1 IK in 4x4 homogeneous transform. Takes offset into account."""
+        if side not in self.exo or not angles:
+            return None
+
+        pin = self.pin
+        q = self.q_exo[side]
+        qmap = self.qmap[side]
+
+        for name, ang in angles.items():
+            idx = qmap.get(name)
+            if idx is not None:
+                q[idx] = float(ang)
+
+        r = self.exo[side]
+        pin.forwardKinematics(r.model, r.data, q)
+        pin.updateFramePlacements(r.model, r.data)
+
+        ee = r.data.oMf[self.ee_id_exo[side]]
+        target = np.eye(4)
+        target[:3, :3] = ee.rotation
+        # offset gets applied in world space
+        cfg = next(a for a in self.arms if a.side == side)
+        target[:3, 3] = cfg.offset + ee.translation
+        return target
+
+    def update_visualization(self):
+        if self.viewer is None or self.markers is None:
+            return
+
+        pin = self.pin
+
+        # g1
+        if self.viz_g1 is not None:
+            self.viz_g1.display(self.q_g1)
+            pin.forwardKinematics(self.robot_g1.model, self.robot_g1.data, self.q_g1)
+            pin.updateFramePlacements(self.robot_g1.model, self.robot_g1.data)
+
+            for a in self.arms:
+                fid = self.ee_id_g1.get(a.side)
+                if fid is None:
+                    continue
+                ee_tf = self.robot_g1.data.oMf[fid].homogeneous
+                p = a.marker_prefix
+                self.markers.tf(f"markers/{p}_g1_ee", ee_tf)
+                self.markers.tf(f"markers/{p}_g1_axes", ee_tf)
+
+        # exos
+        for a in self.arms:
+            side = a.side
+            v = self.viz_exo.get(side)
+            if v is None:
+                continue
+
+            v.display(self.q_exo[side])
+            r = self.exo[side]
+            pin.forwardKinematics(r.model, r.data, self.q_exo[side])
+            pin.updateFramePlacements(r.model, r.data)
+
+            ee = r.data.oMf[self.ee_id_exo[side]]
+            world_tf = (pin.SE3(np.eye(3), a.offset) * ee).homogeneous
+            p = a.marker_prefix
+            self.markers.tf(f"markers/{p}_exo_ee", world_tf)
+            self.markers.tf(f"markers/{p}_exo_axes", world_tf)
+
+            target_tf = np.eye(4)
+            target_tf[:3, :3] = ee.rotation
+            target_tf[:3, 3] = a.offset + ee.translation
+            self.markers.tf(f"markers/{p}_ik_target", target_tf)
+
+    def compute_g1_joints_from_exo(
+        self,
+        left_angles: dict[str, float],
+        right_angles: dict[str, float],
+    ) -> dict[str, float]:
+        """
+        Performs FK on exoskeleton to get end-effector poses in world frame,
+        after which it solves IK on G1 to return joint angles matching those poses in G1 motor order.
+        """
+        pin = self.pin
+
+        targets = {
+            "left": self._fk_target_world("left", left_angles),
+            "right": self._fk_target_world("right", right_angles),
+        }
+
+        # fallback to current g1 ee pose if missing target
+        pin.forwardKinematics(self.robot_g1.model, self.robot_g1.data, self.q_g1)
+        pin.updateFramePlacements(self.robot_g1.model, self.robot_g1.data)
+
+        for a in self.arms:
+            if targets[a.side] is not None:
+                continue
+            fid = self.ee_id_g1.get(a.side)
+            if fid is not None:
+                targets[a.side] = self.robot_g1.data.oMf[fid].homogeneous
+
+        if targets["left"] is None or targets["right"] is None:
+            logger.warning("missing ik targets, returning current pose")
+            return {}
+
+        frozen_vals = {n: self.q_g1[i] for n, i in self.frozen_idx.items()}
+
+        self.q_g1, _ = self.g1_ik.solve_ik(
+            targets["left"], targets["right"], current_lr_arm_motor_q=self.q_g1
+        )
+
+        for n, i in self.frozen_idx.items():
+            self.q_g1[i] = frozen_vals[n]
+
+        return {
+            f"{j.name}.q": float(self.q_g1[i])
+            for i, j in enumerate(G1_29_JointArmIndex)
+            if i < len(self.q_g1)
+        }

src/lerobot/teleoperators/unitree_g1/exo_serial.py

@@ -0,0 +1,119 @@
+#!/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 json
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+
+import serial
+
+from .exo_calib import ExoskeletonCalibration, exo_raw_to_angles, run_exo_calibration
+
+logger = logging.getLogger(__name__)
+
+
+def parse_raw16(line: bytes) -> list[int] | None:
+    try:
+        parts = line.decode("utf-8", errors="ignore").split()
+        if len(parts) < 16:
+            return None
+        return [int(x) for x in parts[:16]]
+    except Exception:
+        return None
+
+
+def read_raw_from_serial(ser) -> list[int] | None:
+    """Read latest sample from serial; if buffer is backed up, keep only the newest."""
+    last = None
+    while ser.in_waiting > 0:
+        b = ser.readline()
+        if not b:
+            break
+        raw16 = parse_raw16(b)
+        if raw16 is not None:
+            last = raw16
+    if last is None:
+        b = ser.readline()
+        if b:
+            last = parse_raw16(b)
+    return last
+
+
+@dataclass
+class ExoskeletonArm:
+    port: str
+    calibration_fpath: Path
+    side: str
+    baud_rate: int = 115200
+
+    _ser: serial.Serial | None = None
+    calibration: ExoskeletonCalibration | None = None
+
+    def __post_init__(self):
+        if self.calibration_fpath.is_file():
+            self._load_calibration()
+
+    @property
+    def is_connected(self) -> bool:
+        return self._ser is not None and getattr(self._ser, "is_open", False)
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.calibration is not None
+
+    def connect(self, calibrate: bool = True) -> None:
+        if self.is_connected:
+            return
+        try:
+            self._ser = serial.Serial(self.port, self.baud_rate, timeout=0.02)
+            self._ser.reset_input_buffer()
+            logger.info(f"connected: {self.port}")
+        except serial.SerialException as e:
+            raise ConnectionError(f"failed to connect to {self.port}: {e}") from e
+
+        if calibrate and not self.is_calibrated:
+            self.calibrate()
+
+    def disconnect(self) -> None:
+        if self._ser:
+            try:
+                self._ser.close()
+            finally:
+                self._ser = None
+
+    def _load_calibration(self) -> None:
+        try:
+            data = json.loads(self.calibration_fpath.read_text())
+            self.calibration = ExoskeletonCalibration.from_dict(data)
+            logger.info(f"loaded calibration: {self.calibration_fpath}")
+        except Exception as e:
+            logger.warning(f"failed to load calibration: {e}")
+
+    def read_raw(self) -> list[int] | None:
+        if not self._ser:
+            return None
+        return read_raw_from_serial(self._ser)
+
+    def get_angles(self) -> dict[str, float]:
+        if not self.calibration:
+            raise RuntimeError("exoskeleton not calibrated")
+        raw = self.read_raw()
+        return {} if raw is None else exo_raw_to_angles(raw, self.calibration)
+
+    def calibrate(self) -> None:
+        ser = self._ser
+        self.calibration = run_exo_calibration(ser, self.side, self.calibration_fpath)

src/lerobot/teleoperators/unitree_g1/unitree_g1.py

@@ -0,0 +1,157 @@
+#!/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 logging
+import time
+from functools import cached_property
+
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS
+
+from ..teleoperator import Teleoperator
+from .config_unitree_g1 import UnitreeG1TeleoperatorConfig
+from .exo_ik import ExoskeletonIKHelper
+from .exo_serial import ExoskeletonArm
+
+logger = logging.getLogger(__name__)
+
+
+class UnitreeG1Teleoperator(Teleoperator):
+    """
+    Bimanual exoskeleton arms teleoperator for Unitree G1 arms.
+
+    Uses inverse kinematics: exoskeleton FK computes end-effector pose,
+    G1 IK solves for joint angles.
+    """
+
+    config_class = UnitreeG1TeleoperatorConfig
+    name = "unitree_g1"
+
+    def __init__(self, config: UnitreeG1TeleoperatorConfig):
+        super().__init__(config)
+        self.config = config
+
+        # Setup calibration directory
+        self.calibration_dir = (
+            config.calibration_dir
+            if config.calibration_dir
+            else HF_LEROBOT_CALIBRATION / TELEOPERATORS / self.name
+        )
+        self.calibration_dir.mkdir(parents=True, exist_ok=True)
+
+        left_id = f"{config.id}_left" if config.id else "left"
+        right_id = f"{config.id}_right" if config.id else "right"
+
+        # Create exoskeleton arm instances
+        self.left_arm = ExoskeletonArm(
+            port=config.left_arm_config.port,
+            baud_rate=config.left_arm_config.baud_rate,
+            calibration_fpath=self.calibration_dir / f"{left_id}.json",
+            side="left",
+        )
+        self.right_arm = ExoskeletonArm(
+            port=config.right_arm_config.port,
+            baud_rate=config.right_arm_config.baud_rate,
+            calibration_fpath=self.calibration_dir / f"{right_id}.json",
+            side="right",
+        )
+
+        self.ik_helper: ExoskeletonIKHelper | None = None
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return {f"{name}.q": float for name in self._g1_joint_names}
+
+    @cached_property
+    def feedback_features(self) -> dict[str, type]:
+        return {}
+
+    @property
+    def is_connected(self) -> bool:
+        return self.left_arm.is_connected and self.right_arm.is_connected
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
+
+    def connect(self, calibrate: bool = True) -> None:
+        self.left_arm.connect(calibrate)
+        self.right_arm.connect(calibrate)
+
+        frozen_joints = [j.strip() for j in self.config.frozen_joints.split(",") if j.strip()]
+        self.ik_helper = ExoskeletonIKHelper(frozen_joints=frozen_joints)
+        logger.info("IK helper initialized")
+
+    def calibrate(self) -> None:
+        if not self.left_arm.is_calibrated:
+            logger.info("Starting calibration for left arm...")
+            self.left_arm.calibrate()
+        else:
+            logger.info("Left arm already calibrated. Skipping.")
+
+        if not self.right_arm.is_calibrated:
+            logger.info("Starting calibration for right arm...")
+            self.right_arm.calibrate()
+        else:
+            logger.info("Right arm already calibrated. Skipping.")
+
+        logger.info("Starting visualization to verify calibration...")
+        self.run_visualization_loop()
+
+    def configure(self) -> None:
+        pass
+
+    def get_action(self) -> dict[str, float]:
+        left_angles = self.left_arm.get_angles()
+        right_angles = self.right_arm.get_angles()
+        return self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles)
+
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        raise NotImplementedError("Exoskeleton arms do not support feedback")
+
+    def disconnect(self) -> None:
+        self.left_arm.disconnect()
+        self.right_arm.disconnect()
+
+    def run_visualization_loop(self):
+        """Run interactive Meshcat visualization loop to verify tracking."""
+        if self.ik_helper is None:
+            frozen_joints = [j.strip() for j in self.config.frozen_joints.split(",") if j.strip()]
+            self.ik_helper = ExoskeletonIKHelper(frozen_joints=frozen_joints)
+
+        self.ik_helper.init_visualization()
+
+        print("\n" + "=" * 60)
+        print("Visualization running! Move the exoskeletons to test tracking.")
+        print("Press Ctrl+C to exit.")
+        print("=" * 60 + "\n")
+
+        try:
+            while True:
+                left_angles = self.left_arm.get_angles()
+                right_angles = self.right_arm.get_angles()
+
+                self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles)
+                self.ik_helper.update_visualization()
+
+                time.sleep(0.01)
+
+        except KeyboardInterrupt:
+            print("\n\nVisualization stopped.")
+
+    @cached_property
+    def _g1_joint_names(self) -> list[str]:
+        return [joint.name for joint in G1_29_JointIndex]

src/lerobot/teleoperators/utils.py

@@ -13,12 +13,14 @@
 # limitations under the License.
 
 from enum import Enum
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 from lerobot.utils.import_utils import make_device_from_device_class
 
 from .config import TeleoperatorConfig
-from .teleoperator import Teleoperator
+
+if TYPE_CHECKING:
+    from .teleoperator import Teleoperator
 
 
 class TeleopEvents(Enum):
@@ -31,7 +33,7 @@ class TeleopEvents(Enum):
     TERMINATE_EPISODE = "terminate_episode"
 
 
-def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
+def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
     # TODO(Steven): Consider just using the make_device_from_device_class for all types
     if config.type == "keyboard":
         from .keyboard import KeyboardTeleop
@@ -73,6 +75,10 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
         from .homunculus import HomunculusArm
 
         return HomunculusArm(config)
+    elif config.type == "unitree_g1":
+        from .unitree_g1 import UnitreeG1Teleoperator
+
+        return UnitreeG1Teleoperator(config)
     elif config.type == "bi_so_leader":
         from .bi_so_leader import BiSOLeader
 
@@ -81,8 +87,16 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator:
         from .reachy2_teleoperator import Reachy2Teleoperator
 
         return Reachy2Teleoperator(config)
+    elif config.type == "openarm_leader":
+        from .openarm_leader import OpenArmLeader
+
+        return OpenArmLeader(config)
+    elif config.type == "bi_openarm_leader":
+        from .bi_openarm_leader import BiOpenArmLeader
+
+        return BiOpenArmLeader(config)
     else:
         try:
-            return cast(Teleoperator, make_device_from_device_class(config))
+            return cast("Teleoperator", make_device_from_device_class(config))
         except Exception as e:
             raise ValueError(f"Error creating robot with config {config}: {e}") from e

src/lerobot/utils/constants.py

@@ -26,6 +26,9 @@ OBS_IMAGES = OBS_IMAGE + "s"
 OBS_LANGUAGE = OBS_STR + ".language"
 OBS_LANGUAGE_TOKENS = OBS_LANGUAGE + ".tokens"
 OBS_LANGUAGE_ATTENTION_MASK = OBS_LANGUAGE + ".attention_mask"
+OBS_LANGUAGE_SUBTASK = OBS_STR + ".subtask"
+OBS_LANGUAGE_SUBTASK_TOKENS = OBS_LANGUAGE_SUBTASK + ".tokens"
+OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK + ".attention_mask"
 
 ACTION = "action"
 ACTION_PREFIX = ACTION + "."

tests/cameras/test_opencv.py

@@ -20,7 +20,9 @@
 # ```
 
 from pathlib import Path
+from unittest.mock import patch
 
+import cv2
 import numpy as np
 import pytest
 
@@ -28,6 +30,50 @@ from lerobot.cameras.configs import Cv2Rotation
 from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig
 from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
 
+RealVideoCapture = cv2.VideoCapture
+
+
+class MockLoopingVideoCapture:
+    """
+    Wraps the real OpenCV VideoCapture.
+    Motivation: cv2.VideoCapture(file.png) is only valid for one read.
+    Strategy: Read the file once & return the cached frame for subsequent reads.
+    Consequence: No recurrent I/O operations, but we keep the test artifacts simple.
+    """
+
+    def __init__(self, *args, **kwargs):
+        args_clean = [str(a) if isinstance(a, Path) else a for a in args]
+        self._real_vc = RealVideoCapture(*args_clean, **kwargs)
+        self._cached_frame = None
+
+    def read(self):
+        ret, frame = self._real_vc.read()
+
+        if ret:
+            self._cached_frame = frame
+            return ret, frame
+
+        if not ret and self._cached_frame is not None:
+            return True, self._cached_frame.copy()
+
+        return ret, frame
+
+    def __getattr__(self, name):
+        return getattr(self._real_vc, name)
+
+
+@pytest.fixture(autouse=True)
+def patch_opencv_videocapture():
+    """
+    Automatically patches cv2.VideoCapture for all tests.
+    """
+    module_path = OpenCVCamera.__module__
+    target = f"{module_path}.cv2.VideoCapture"
+
+    with patch(target, new=MockLoopingVideoCapture):
+        yield
+
+
 # NOTE(Steven): more tests + assertions?
 TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras"
 DEFAULT_PNG_FILE_PATH = TEST_ARTIFACTS_DIR / "image_160x120.png"
@@ -43,25 +89,22 @@ def test_abc_implementation():
 
 
 def test_connect():
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
-    camera = OpenCVCamera(config)
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
 
-    camera.connect(warmup=False)
-
-    assert camera.is_connected
+    with OpenCVCamera(config) as camera:
+        assert camera.is_connected
 
 
 def test_connect_already_connected():
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
-    camera = OpenCVCamera(config)
-    camera.connect(warmup=False)
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
 
-    with pytest.raises(DeviceAlreadyConnectedError):
-        camera.connect(warmup=False)
+    with OpenCVCamera(config) as camera, pytest.raises(DeviceAlreadyConnectedError):
+        camera.connect()
 
 
 def test_connect_invalid_camera_path():
     config = OpenCVCameraConfig(index_or_path="nonexistent/camera.png")
+
     camera = OpenCVCamera(config)
 
     with pytest.raises(ConnectionError):
@@ -74,27 +117,25 @@ def test_invalid_width_connect():
         width=99999,  # Invalid width to trigger error
         height=480,
     )
-    camera = OpenCVCamera(config)
 
+    camera = OpenCVCamera(config)
     with pytest.raises(RuntimeError):
         camera.connect(warmup=False)
 
 
 @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
 def test_read(index_or_path):
-    config = OpenCVCameraConfig(index_or_path=index_or_path)
-    camera = OpenCVCamera(config)
-    camera.connect(warmup=False)
+    config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0)
 
-    img = camera.read()
-
-    assert isinstance(img, np.ndarray)
+    with OpenCVCamera(config) as camera:
+        img = camera.read()
+        assert isinstance(img, np.ndarray)
 
 
 def test_read_before_connect():
     config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
-    camera = OpenCVCamera(config)
 
+    camera = OpenCVCamera(config)
     with pytest.raises(DeviceNotConnectedError):
         _ = camera.read()
 
@@ -119,32 +160,22 @@ def test_disconnect_before_connect():
 
 @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
 def test_async_read(index_or_path):
-    config = OpenCVCameraConfig(index_or_path=index_or_path)
-    camera = OpenCVCamera(config)
-    camera.connect(warmup=False)
+    config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0)
 
-    try:
+    with OpenCVCamera(config) as camera:
         img = camera.async_read()
 
         assert camera.thread is not None
         assert camera.thread.is_alive()
         assert isinstance(img, np.ndarray)
-    finally:
-        if camera.is_connected:
-            camera.disconnect()  # To stop/join the thread. Otherwise get warnings when the test ends
 
 
 def test_async_read_timeout():
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
-    camera = OpenCVCamera(config)
-    camera.connect(warmup=False)
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
 
-    try:
-        with pytest.raises(TimeoutError):
-            camera.async_read(timeout_ms=0)
-    finally:
-        if camera.is_connected:
-            camera.disconnect()
+    with OpenCVCamera(config) as camera, pytest.raises(TimeoutError):
+        camera.async_read(timeout_ms=0)  # consumes any available frame by then
+        camera.async_read(timeout_ms=0)  # request immediately another one
 
 
 def test_async_read_before_connect():
@@ -155,6 +186,50 @@ def test_async_read_before_connect():
         _ = camera.async_read()
 
 
+def test_read_latest():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
+
+    with OpenCVCamera(config) as camera:
+        # ensure at least one fresh frame is captured
+        frame = camera.read()
+        latest = camera.read_latest()
+
+        assert isinstance(latest, np.ndarray)
+        assert latest.shape == frame.shape
+
+
+def test_read_latest_before_connect():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH)
+
+    camera = OpenCVCamera(config)
+    with pytest.raises(DeviceNotConnectedError):
+        _ = camera.read_latest()
+
+
+def test_read_latest_high_frequency():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
+
+    with OpenCVCamera(config) as camera:
+        # prime to ensure frames are available
+        ref = camera.read()
+
+        for _ in range(20):
+            latest = camera.read_latest()
+            assert isinstance(latest, np.ndarray)
+            assert latest.shape == ref.shape
+
+
+def test_read_latest_too_old():
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0)
+
+    with OpenCVCamera(config) as camera:
+        # prime to ensure frames are available
+        _ = camera.read()
+
+        with pytest.raises(TimeoutError):
+            _ = camera.read_latest(max_age_ms=0)  # immediately too old
+
+
 def test_fourcc_configuration():
     """Test FourCC configuration validation and application."""
 
@@ -181,18 +256,15 @@ def test_fourcc_configuration():
 
 def test_fourcc_with_camera():
     """Test FourCC functionality with actual camera connection."""
-    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, fourcc="MJPG")
-    camera = OpenCVCamera(config)
+    config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, fourcc="MJPG", warmup_s=0)
 
     # Connect should work with MJPG specified
-    camera.connect(warmup=False)
-    assert camera.is_connected
+    with OpenCVCamera(config) as camera:
+        assert camera.is_connected
 
-    # Read should work normally
-    img = camera.read()
-    assert isinstance(img, np.ndarray)
-
-    camera.disconnect()
+        # Read should work normally
+        img = camera.read()
+        assert isinstance(img, np.ndarray)
 
 
 @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES)
@@ -211,18 +283,16 @@ def test_rotation(rotation, index_or_path):
     dimensions = filename.split("_")[-1].split(".")[0]  # Assumes filenames format (_wxh.png)
     original_width, original_height = map(int, dimensions.split("x"))
 
-    config = OpenCVCameraConfig(index_or_path=index_or_path, rotation=rotation)
-    camera = OpenCVCamera(config)
-    camera.connect(warmup=False)
+    config = OpenCVCameraConfig(index_or_path=index_or_path, rotation=rotation, warmup_s=0)
+    with OpenCVCamera(config) as camera:
+        img = camera.read()
+        assert isinstance(img, np.ndarray)
 
-    img = camera.read()
-    assert isinstance(img, np.ndarray)
-
-    if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270):
-        assert camera.width == original_height
-        assert camera.height == original_width
-        assert img.shape[:2] == (original_width, original_height)
-    else:
-        assert camera.width == original_width
-        assert camera.height == original_height
-        assert img.shape[:2] == (original_height, original_width)
+        if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270):
+            assert camera.width == original_height
+            assert camera.height == original_width
+            assert img.shape[:2] == (original_width, original_height)
+        else:
+            assert camera.width == original_width
+            assert camera.height == original_height
+            assert img.shape[:2] == (original_height, original_width)

tests/cameras/test_reachy2_camera.py

@@ -150,6 +150,44 @@ def test_async_read_before_connect(camera):
         _ = camera.async_read()
 
 
+def test_read_latest(camera):
+    camera.connect()
+
+    frame = camera.read()
+    latest = camera.read_latest()
+
+    assert isinstance(latest, np.ndarray)
+    assert latest.shape == frame.shape
+
+
+def test_read_latest_before_connect(camera):
+    # camera fixture yields an unconnected camera instance
+    with pytest.raises(DeviceNotConnectedError):
+        _ = camera.read_latest()
+
+
+def test_read_latest_high_frequency(camera):
+    camera.connect()
+
+    # prime to ensure frames are available
+    ref = camera.read()
+
+    for _ in range(20):
+        latest = camera.read_latest()
+        assert isinstance(latest, np.ndarray)
+        assert latest.shape == ref.shape
+
+
+def test_read_latest_too_old(camera):
+    camera.connect()
+
+    # prime to ensure frames are available
+    _ = camera.read()
+
+    with pytest.raises(TimeoutError):
+        _ = camera.read_latest(max_age_ms=0)  # immediately too old
+
+
 def test_wrong_camera_name():
     with pytest.raises(ValueError):
         _ = Reachy2CameraConfig(name="wrong-name", image_type="left")

tests/cameras/test_realsense.py

@@ -62,19 +62,15 @@ def test_abc_implementation():
 
 
 def test_connect():
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
+    config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0)
 
-    camera.connect(warmup=False)
-    assert camera.is_connected
+    with RealSenseCamera(config) as camera:
+        assert camera.is_connected
 
 
 def test_connect_already_connected():
-    config = RealSenseCameraConfig(serial_number_or_name="042")
-    camera = RealSenseCamera(config)
-    camera.connect(warmup=False)
-
-    with pytest.raises(DeviceAlreadyConnectedError):
+    config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0)
+    with RealSenseCamera(config) as camera, pytest.raises(DeviceAlreadyConnectedError):
         camera.connect(warmup=False)
 
 
@@ -96,12 +92,10 @@ def test_invalid_width_connect():
 
 
 def test_read():
-    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
-    camera = RealSenseCamera(config)
-    camera.connect(warmup=False)
-
-    img = camera.read()
-    assert isinstance(img, np.ndarray)
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
+    with RealSenseCamera(config) as camera:
+        img = camera.read()
+        assert isinstance(img, np.ndarray)
 
 
 # TODO(Steven): Fix this test for the latest version of pyrealsense2.
@@ -142,32 +136,21 @@ def test_disconnect_before_connect():
 
 
 def test_async_read():
-    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
-    camera = RealSenseCamera(config)
-    camera.connect(warmup=False)
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
 
-    try:
+    with RealSenseCamera(config) as camera:
         img = camera.async_read()
 
         assert camera.thread is not None
         assert camera.thread.is_alive()
         assert isinstance(img, np.ndarray)
-    finally:
-        if camera.is_connected:
-            camera.disconnect()  # To stop/join the thread. Otherwise get warnings when the test ends
 
 
 def test_async_read_timeout():
-    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30)
-    camera = RealSenseCamera(config)
-    camera.connect(warmup=False)
-
-    try:
-        with pytest.raises(TimeoutError):
-            camera.async_read(timeout_ms=0)
-    finally:
-        if camera.is_connected:
-            camera.disconnect()
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
+    with RealSenseCamera(config) as camera, pytest.raises(TimeoutError):
+        camera.async_read(timeout_ms=0)  # consumes any available frame by then
+        camera.async_read(timeout_ms=0)  # request immediately another one
 
 
 def test_async_read_before_connect():
@@ -178,6 +161,47 @@ def test_async_read_before_connect():
         _ = camera.async_read()
 
 
+def test_read_latest():
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
+    with RealSenseCamera(config) as camera:
+        img = camera.read()
+        latest = camera.read_latest()
+
+        assert isinstance(latest, np.ndarray)
+        assert latest.shape == img.shape
+
+
+def test_read_latest_high_frequency():
+    config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0)
+    with RealSenseCamera(config) as camera:
+        # prime with one read to ensure frames are available
+        ref = camera.read()
+
+        for _ in range(20):
+            latest = camera.read_latest()
+            assert isinstance(latest, np.ndarray)
+            assert latest.shape == ref.shape
+
+
+def test_read_latest_before_connect():
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+    camera = RealSenseCamera(config)
+
+    with pytest.raises(DeviceNotConnectedError):
+        _ = camera.read_latest()
+
+
+def test_read_latest_too_old():
+    config = RealSenseCameraConfig(serial_number_or_name="042")
+
+    with RealSenseCamera(config) as camera:
+        # prime to ensure frames are available
+        _ = camera.read()
+
+        with pytest.raises(TimeoutError):
+            _ = camera.read_latest(max_age_ms=0)  # immediately too old
+
+
 @pytest.mark.parametrize(
     "rotation",
     [
@@ -189,18 +213,16 @@ def test_async_read_before_connect():
     ids=["no_rot", "rot90", "rot180", "rot270"],
 )
 def test_rotation(rotation):
-    config = RealSenseCameraConfig(serial_number_or_name="042", rotation=rotation)
-    camera = RealSenseCamera(config)
-    camera.connect(warmup=False)
+    config = RealSenseCameraConfig(serial_number_or_name="042", rotation=rotation, warmup_s=0)
+    with RealSenseCamera(config) as camera:
+        img = camera.read()
+        assert isinstance(img, np.ndarray)
 
-    img = camera.read()
-    assert isinstance(img, np.ndarray)
-
-    if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270):
-        assert camera.width == 480
-        assert camera.height == 640
-        assert img.shape[:2] == (640, 480)
-    else:
-        assert camera.width == 640
-        assert camera.height == 480
-        assert img.shape[:2] == (480, 640)
+        if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270):
+            assert camera.width == 480
+            assert camera.height == 640
+            assert img.shape[:2] == (640, 480)
+        else:
+            assert camera.width == 640
+            assert camera.height == 480
+            assert img.shape[:2] == (480, 640)

tests/datasets/test_aggregate.py

@@ -525,3 +525,92 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
         assert img.shape[0] == 3, f"Image {image_key} should have 3 channels"
 
     assert_dataset_iteration_works(aggr_ds)
+
+
+def test_aggregate_already_merged_dataset(tmp_path, lerobot_dataset_factory):
+    """Regression test for aggregating a dataset that is itself a result of a previous merge.
+
+    This test reproduces the bug where merging datasets with multiple parquet files
+    (e.g., from a previous merge with file rotation) would cause FileNotFoundError
+    because metadata file indices were incorrectly preserved instead of being mapped
+    to their actual destination files.
+
+    The fix adds src_to_dst tracking in aggregate_data() to correctly map source
+    file indices to destination file indices.
+    """
+    # Step 1: Create datasets A and B
+    ds_a = lerobot_dataset_factory(
+        root=tmp_path / "ds_a",
+        repo_id=f"{DUMMY_REPO_ID}_a",
+        total_episodes=4,
+        total_frames=200,
+    )
+    ds_b = lerobot_dataset_factory(
+        root=tmp_path / "ds_b",
+        repo_id=f"{DUMMY_REPO_ID}_b",
+        total_episodes=4,
+        total_frames=200,
+    )
+
+    # Step 2: Merge A+B into AB with small file size to force multiple files
+    aggregate_datasets(
+        repo_ids=[ds_a.repo_id, ds_b.repo_id],
+        roots=[ds_a.root, ds_b.root],
+        aggr_repo_id=f"{DUMMY_REPO_ID}_ab",
+        aggr_root=tmp_path / "ds_ab",
+        data_files_size_in_mb=0.01,  # Force file rotation
+    )
+
+    with (
+        patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version,
+        patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download,
+    ):
+        mock_get_safe_version.return_value = "v3.0"
+        mock_snapshot_download.return_value = str(tmp_path / "ds_ab")
+        ds_ab = LeRobotDataset(f"{DUMMY_REPO_ID}_ab", root=tmp_path / "ds_ab")
+
+    # Verify AB has multiple data files (file rotation occurred)
+    ab_data_files = list((tmp_path / "ds_ab" / "data").rglob("*.parquet"))
+    assert len(ab_data_files) > 1, "First merge should create multiple parquet files"
+
+    # Step 3: Create dataset C
+    ds_c = lerobot_dataset_factory(
+        root=tmp_path / "ds_c",
+        repo_id=f"{DUMMY_REPO_ID}_c",
+        total_episodes=2,
+        total_frames=100,
+    )
+
+    # Step 4: Merge AB+C into final - THIS IS WHERE THE BUG OCCURRED
+    aggregate_datasets(
+        repo_ids=[ds_ab.repo_id, ds_c.repo_id],
+        roots=[ds_ab.root, ds_c.root],
+        aggr_repo_id=f"{DUMMY_REPO_ID}_abc",
+        aggr_root=tmp_path / "ds_abc",
+    )
+
+    with (
+        patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version,
+        patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download,
+    ):
+        mock_get_safe_version.return_value = "v3.0"
+        mock_snapshot_download.return_value = str(tmp_path / "ds_abc")
+        ds_abc = LeRobotDataset(f"{DUMMY_REPO_ID}_abc", root=tmp_path / "ds_abc")
+
+    # Step 5: Verify all data files referenced in metadata actually exist
+    for ep_idx in range(ds_abc.num_episodes):
+        data_file_path = ds_abc.root / ds_abc.meta.get_data_file_path(ep_idx)
+        assert data_file_path.exists(), (
+            f"Episode {ep_idx} references non-existent file: {data_file_path}\n"
+            "This indicates the src_to_dst mapping fix is not working correctly."
+        )
+
+    # Step 6: Verify we can iterate through the entire dataset without FileNotFoundError
+    expected_episodes = ds_a.num_episodes + ds_b.num_episodes + ds_c.num_episodes
+    expected_frames = ds_a.num_frames + ds_b.num_frames + ds_c.num_frames
+
+    assert ds_abc.num_episodes == expected_episodes
+    assert ds_abc.num_frames == expected_frames
+
+    # This would raise FileNotFoundError before the fix
+    assert_dataset_iteration_works(ds_abc)

tests/datasets/test_dataset_tools.py

@@ -26,6 +26,7 @@ from lerobot.datasets.dataset_tools import (
     delete_episodes,
     merge_datasets,
     modify_features,
+    modify_tasks,
     remove_feature,
     split_dataset,
 )
@@ -1050,6 +1051,174 @@ def test_modify_features_preserves_file_structure(sample_dataset, tmp_path):
         assert "reward" in modified_dataset.meta.features
 
 
+def test_modify_tasks_single_task_for_all(sample_dataset):
+    """Test setting a single task for all episodes."""
+    new_task = "Pick up the cube and place it"
+
+    modified_dataset = modify_tasks(sample_dataset, new_task=new_task)
+
+    # Verify all episodes have the new task
+    assert len(modified_dataset.meta.tasks) == 1
+    assert new_task in modified_dataset.meta.tasks.index
+
+    # Verify task_index is 0 for all frames (only one task)
+    for i in range(len(modified_dataset)):
+        item = modified_dataset[i]
+        assert item["task_index"].item() == 0
+        assert item["task"] == new_task
+
+
+def test_modify_tasks_episode_specific(sample_dataset):
+    """Test setting different tasks for specific episodes."""
+    episode_tasks = {
+        0: "Task A",
+        1: "Task B",
+        2: "Task A",
+        3: "Task C",
+        4: "Task B",
+    }
+
+    modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks)
+
+    # Verify correct number of unique tasks
+    unique_tasks = set(episode_tasks.values())
+    assert len(modified_dataset.meta.tasks) == len(unique_tasks)
+
+    # Verify each episode has the correct task
+    for ep_idx, expected_task in episode_tasks.items():
+        ep_data = modified_dataset.meta.episodes[ep_idx]
+        assert ep_data["tasks"][0] == expected_task
+
+
+def test_modify_tasks_default_with_overrides(sample_dataset):
+    """Test setting a default task with specific overrides."""
+    default_task = "Default task"
+    override_task = "Special task"
+    episode_tasks = {2: override_task, 4: override_task}
+
+    modified_dataset = modify_tasks(
+        sample_dataset,
+        new_task=default_task,
+        episode_tasks=episode_tasks,
+    )
+
+    # Verify correct number of unique tasks
+    assert len(modified_dataset.meta.tasks) == 2
+    assert default_task in modified_dataset.meta.tasks.index
+    assert override_task in modified_dataset.meta.tasks.index
+
+    # Verify episodes have correct tasks
+    for ep_idx in range(5):
+        ep_data = modified_dataset.meta.episodes[ep_idx]
+        if ep_idx in episode_tasks:
+            assert ep_data["tasks"][0] == override_task
+        else:
+            assert ep_data["tasks"][0] == default_task
+
+
+def test_modify_tasks_no_task_specified(sample_dataset):
+    """Test error when no task is specified."""
+    with pytest.raises(ValueError, match="Must specify at least one of new_task or episode_tasks"):
+        modify_tasks(sample_dataset)
+
+
+def test_modify_tasks_invalid_episode_indices(sample_dataset):
+    """Test error with invalid episode indices."""
+    with pytest.raises(ValueError, match="Invalid episode indices"):
+        modify_tasks(sample_dataset, episode_tasks={10: "Task", 20: "Task"})
+
+
+def test_modify_tasks_updates_info_json(sample_dataset):
+    """Test that total_tasks is updated in info.json."""
+    episode_tasks = {0: "Task A", 1: "Task B", 2: "Task C", 3: "Task A", 4: "Task B"}
+
+    modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks)
+
+    # Verify total_tasks is updated
+    assert modified_dataset.meta.total_tasks == 3
+
+
+def test_modify_tasks_preserves_other_metadata(sample_dataset):
+    """Test that modifying tasks preserves other metadata."""
+    original_frames = sample_dataset.meta.total_frames
+    original_episodes = sample_dataset.meta.total_episodes
+    original_fps = sample_dataset.meta.fps
+
+    modified_dataset = modify_tasks(sample_dataset, new_task="New task")
+
+    # Verify other metadata is preserved
+    assert modified_dataset.meta.total_frames == original_frames
+    assert modified_dataset.meta.total_episodes == original_episodes
+    assert modified_dataset.meta.fps == original_fps
+
+
+def test_modify_tasks_task_index_correct(sample_dataset):
+    """Test that task_index values are correct in data files."""
+    # Create tasks that will have predictable indices (sorted alphabetically)
+    episode_tasks = {
+        0: "Alpha task",  # Will be index 0
+        1: "Beta task",  # Will be index 1
+        2: "Alpha task",  # Will be index 0
+        3: "Gamma task",  # Will be index 2
+        4: "Beta task",  # Will be index 1
+    }
+
+    modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks)
+
+    # Verify task indices are correct
+    task_to_expected_idx = {
+        "Alpha task": 0,
+        "Beta task": 1,
+        "Gamma task": 2,
+    }
+
+    for i in range(len(modified_dataset)):
+        item = modified_dataset[i]
+        ep_idx = item["episode_index"].item()
+        expected_task = episode_tasks[ep_idx]
+        expected_idx = task_to_expected_idx[expected_task]
+        assert item["task_index"].item() == expected_idx
+        assert item["task"] == expected_task
+
+
+def test_modify_tasks_in_place(sample_dataset):
+    """Test that modify_tasks modifies the dataset in-place."""
+    original_root = sample_dataset.root
+
+    modified_dataset = modify_tasks(sample_dataset, new_task="New task")
+
+    # Verify same instance is returned and root is unchanged
+    assert modified_dataset is sample_dataset
+    assert modified_dataset.root == original_root
+
+
+def test_modify_tasks_keeps_original_when_not_overridden(sample_dataset):
+    """Test that original tasks are kept when using episode_tasks without new_task."""
+    from lerobot.datasets.utils import load_episodes
+
+    # Ensure episodes metadata is loaded
+    if sample_dataset.meta.episodes is None:
+        sample_dataset.meta.episodes = load_episodes(sample_dataset.meta.root)
+
+    # Get original tasks for episodes not being overridden
+    original_task_ep0 = sample_dataset.meta.episodes[0]["tasks"][0]
+    original_task_ep1 = sample_dataset.meta.episodes[1]["tasks"][0]
+
+    # Only override episodes 2, 3, 4
+    episode_tasks = {2: "New Task A", 3: "New Task B", 4: "New Task A"}
+
+    modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks)
+
+    # Verify original tasks are kept for episodes 0 and 1
+    assert modified_dataset.meta.episodes[0]["tasks"][0] == original_task_ep0
+    assert modified_dataset.meta.episodes[1]["tasks"][0] == original_task_ep1
+
+    # Verify new tasks for overridden episodes
+    assert modified_dataset.meta.episodes[2]["tasks"][0] == "New Task A"
+    assert modified_dataset.meta.episodes[3]["tasks"][0] == "New Task B"
+    assert modified_dataset.meta.episodes[4]["tasks"][0] == "New Task A"
+
+
 def test_convert_image_to_video_dataset(tmp_path):
     """Test converting lerobot/pusht_image dataset to video format."""
     from lerobot.datasets.lerobot_dataset import LeRobotDataset

tests/datasets/test_image_transforms.py

@@ -390,6 +390,30 @@ def test_sharpness_jitter_invalid_range_max_smaller():
         SharpnessJitter((2.0, 0.1))
 
 
+def test_make_transform_from_config_with_v2_resize(img_tensor_factory):
+    img_tensor = img_tensor_factory()
+    tf_cfg = ImageTransformConfig(type="Resize", kwargs={"size": (32, 32)})
+    tf = make_transform_from_config(tf_cfg)
+    assert isinstance(tf, v2.Resize)
+    output = tf(img_tensor)
+    assert output.shape[-2:] == (32, 32)
+
+
+def test_make_transform_from_config_with_v2_identity(img_tensor_factory):
+    img_tensor = img_tensor_factory()
+    tf_cfg = ImageTransformConfig(type="Identity", kwargs={})
+    tf = make_transform_from_config(tf_cfg)
+    assert isinstance(tf, v2.Identity)
+    output = tf(img_tensor)
+    assert output.shape == img_tensor.shape
+
+
+def test_make_transform_from_config_invalid_type():
+    tf_cfg = ImageTransformConfig(type="NotARealTransform", kwargs={})
+    with pytest.raises(ValueError, match="not valid"):
+        make_transform_from_config(tf_cfg)
+
+
 def test_save_all_transforms(img_tensor_factory, tmp_path):
     img_tensor = img_tensor_factory()
     tf_cfg = ImageTransformsConfig(enable=True)