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/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
 

pyproject.toml

@@ -360,9 +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/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,
@@ -117,7 +118,7 @@ class OpenCVCamera(Camera):
         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
@@ -132,6 +133,7 @@ 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.
@@ -148,8 +150,6 @@ class OpenCVCamera(Camera):
             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.
@@ -178,6 +178,7 @@ class OpenCVCamera(Camera):
 
         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.
@@ -197,8 +198,6 @@ class OpenCVCamera(Camera):
                           to the requested value.
             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:
@@ -348,6 +347,7 @@ class OpenCVCamera(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.
@@ -374,9 +374,6 @@ class OpenCVCamera(Camera):
                 f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
             )
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -490,6 +487,7 @@ class OpenCVCamera(Camera):
             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.
@@ -512,8 +510,6 @@ 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():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -533,6 +529,7 @@ 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).
 
@@ -548,8 +545,6 @@ class OpenCVCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

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:
@@ -123,6 +124,7 @@ 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.
@@ -136,9 +138,6 @@ 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.")
 
@@ -184,6 +183,7 @@ class Reachy2Camera(Camera):
 
         return frame
 
+    @check_if_not_connected
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
         """
         Same as read()
@@ -197,11 +197,10 @@ 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.")
 
         return self.read()
 
+    @check_if_not_connected
     def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]:
         """Return the most recent frame captured immediately (Peeking).
 
@@ -219,8 +218,6 @@ class Reachy2Camera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.latest_frame is None or self.latest_timestamp is None:
             raise RuntimeError(f"{self} has not captured any frames yet.")
@@ -233,6 +230,7 @@ class Reachy2Camera(Camera):
 
         return self.latest_frame
 
+    @check_if_not_connected
     def disconnect(self) -> None:
         """
         Stops the background read thread (if running).
@@ -240,8 +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
@@ -152,6 +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.
@@ -169,8 +171,6 @@ class RealSenseCamera(Camera):
             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()
@@ -290,6 +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.
 
@@ -299,8 +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.")
@@ -320,6 +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.
@@ -345,9 +345,6 @@ class RealSenseCamera(Camera):
                 f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}."
             )
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -374,6 +371,7 @@ class RealSenseCamera(Camera):
 
         return frame
 
+    @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.
@@ -403,9 +401,6 @@ class RealSenseCamera(Camera):
                 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 self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -534,6 +529,7 @@ class RealSenseCamera(Camera):
             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.
@@ -556,8 +552,6 @@ 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():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -578,6 +572,7 @@ 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).
 
@@ -593,8 +588,6 @@ class RealSenseCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

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
@@ -104,6 +105,7 @@ class ZMQCamera(Camera):
         """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.
 
@@ -111,8 +113,6 @@ class ZMQCamera(Camera):
             warmup (bool): If True, waits for the camera to provide at least one
                            valid frame before returning. Defaults to True.
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} is already connected.")
 
         logger.info(f"Connecting to {self}...")
 
@@ -211,6 +211,7 @@ 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.
@@ -228,9 +229,6 @@ class ZMQCamera(Camera):
                 f"{self} read() color_mode parameter is deprecated and will be removed in future versions."
             )
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
 
@@ -301,6 +299,7 @@ class ZMQCamera(Camera):
             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.
@@ -317,8 +316,6 @@ class ZMQCamera(Camera):
             TimeoutError: If no frame data becomes available within the specified timeout.
             RuntimeError: If the background thread is not running.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")
@@ -335,6 +332,7 @@ 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).
 
@@ -350,8 +348,6 @@ class ZMQCamera(Camera):
             DeviceNotConnectedError: If the camera is not connected.
             RuntimeError: If the camera is connected but has not captured any frames yet.
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         if self.thread is None or not self.thread.is_alive():
             raise RuntimeError(f"{self} read thread is not running.")

src/lerobot/cameras/zmq/configuration_zmq.py

@@ -32,10 +32,7 @@ class ZMQCameraConfig(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."
-            )
+        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/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

@@ -23,6 +23,7 @@ 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:
@@ -36,7 +37,6 @@ else:
 
 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
 
@@ -155,6 +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.
@@ -162,10 +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
@@ -211,6 +208,9 @@ class DamiaoMotorsBus(MotorsBusBase):
         logger.info("Starting handshake with 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
 
@@ -246,6 +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.
@@ -253,8 +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:
@@ -283,6 +282,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         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, 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)
@@ -341,6 +344,10 @@ class DamiaoMotorsBus(MotorsBusBase):
         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, 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)
 
@@ -356,6 +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 = []
@@ -394,10 +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
@@ -461,6 +475,9 @@ 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, is_fd=self.use_can_fd)
         self.canbus.send(msg)
@@ -488,6 +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)
@@ -562,10 +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)
@@ -595,6 +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,
@@ -605,8 +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)
@@ -656,6 +674,10 @@ 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)
@@ -678,10 +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():
@@ -690,6 +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)
@@ -732,9 +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/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/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/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -19,6 +19,7 @@ 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
@@ -112,6 +113,7 @@ class BiOpenArmFollower(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)
@@ -133,6 +135,7 @@ class BiOpenArmFollower(Robot):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
+    @check_if_not_connected
     def get_observation(self) -> RobotObservation:
         obs_dict = {}
 
@@ -146,6 +149,7 @@ class BiOpenArmFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -170,6 +174,7 @@ class BiOpenArmFollower(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/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/openarm_follower.py

@@ -23,7 +23,7 @@ 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.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..robot import Robot
 from ..utils import ensure_safe_goal_position
@@ -119,6 +119,7 @@ class OpenArmFollower(Robot):
         """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.
@@ -126,8 +127,6 @@ class OpenArmFollower(Robot):
         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.
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} already connected")
 
         # Connect to CAN bus
         logger.info(f"Connecting arm on {self.config.port}...")
@@ -219,6 +218,7 @@ class OpenArmFollower(Robot):
             "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.
@@ -228,9 +228,6 @@ class OpenArmFollower(Robot):
         """
         start = time.perf_counter()
 
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
-
         obs_dict: dict[str, Any] = {}
 
         states = self.bus.sync_read_all_states()
@@ -253,6 +250,7 @@ class OpenArmFollower(Robot):
 
         return obs_dict
 
+    @check_if_not_connected
     def send_action(
         self,
         action: RobotAction,
@@ -272,8 +270,6 @@ class OpenArmFollower(Robot):
         Returns:
             The action actually sent (potentially clipped)
         """
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
 
@@ -333,10 +329,9 @@ class OpenArmFollower(Robot):
 
         return {f"{motor}.pos": val for motor, val in goal_pos.items()}
 
+    @check_if_not_connected
     def disconnect(self):
         """Disconnect from robot."""
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Disconnect CAN bus
         self.bus.disconnect(self.config.disable_torque_on_disconnect)

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_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/bi_openarm_leader.py

@@ -19,6 +19,7 @@ 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
@@ -88,6 +89,7 @@ class BiOpenArmLeader(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)
@@ -109,6 +111,7 @@ class BiOpenArmLeader(Teleoperator):
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
         )
 
+    @check_if_not_connected
     def get_action(self) -> RobotAction:
         action_dict = {}
 
@@ -126,6 +129,7 @@ class BiOpenArmLeader(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/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/openarm_leader.py

@@ -21,7 +21,7 @@ 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.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
 from ..teleoperator import Teleoperator
 from .config_openarm_leader import OpenArmLeaderConfig
@@ -84,6 +84,7 @@ class OpenArmLeader(Teleoperator):
         """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.
@@ -91,8 +92,6 @@ class OpenArmLeader(Teleoperator):
         For manual control, we disable torque after connecting so the
         arm can be moved by hand.
         """
-        if self.is_connected:
-            raise DeviceAlreadyConnectedError(f"{self} already connected")
 
         # Connect to CAN bus
         logger.info(f"Connecting arm on {self.config.port}...")
@@ -183,6 +182,7 @@ class OpenArmLeader(Teleoperator):
             "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.
@@ -193,8 +193,6 @@ class OpenArmLeader(Teleoperator):
         Reads all motor states (pos/vel/torque) in one CAN refresh cycle.
         """
         start = time.perf_counter()
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         action_dict: dict[str, Any] = {}
 
@@ -214,10 +212,9 @@ class OpenArmLeader(Teleoperator):
     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."""
-        if not self.is_connected:
-            raise DeviceNotConnectedError(f"{self} is not connected.")
 
         # Disconnect CAN bus
         # For manual control, ensure torque is disabled before disconnecting