add trim_episode_start dataset edit operation

Add a dataset edit operation to trim the first N seconds from episodes while rebuilding frame and episode indices and metadata consistently. Skip episodes that are too short to trim and cover parsing plus metadata invariants with focused tests.

Made-with: Cursor

.github/workflows/fast_tests.yml

@@ -44,7 +44,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:

.github/workflows/full_tests.yml

@@ -37,7 +37,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 
 # Ensures that only the latest action is built, canceling older runs.
@@ -185,7 +185,7 @@ jobs:
           hf auth login --token "$HF_USER_TOKEN" --add-to-git-credential
           hf auth whoami
       - name: Fix ptxas permissions
-        run: chmod +x /lerobot/.venv/lib/python3.10/site-packages/triton/backends/nvidia/bin/ptxas
+        run: chmod +x /lerobot/.venv/lib/python3.12/site-packages/triton/backends/nvidia/bin/ptxas
       - name: Run pytest on GPU
         run: pytest tests -vv --maxfail=10
       - name: Run end-to-end tests

.github/workflows/nightly.yml

@@ -28,7 +28,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
   DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest
 
@@ -206,5 +206,4 @@ jobs:
           python -c "import torch; print(f'PyTorch CUDA available: {torch.cuda.is_available()}'); print(f'Number of GPUs: {torch.cuda.device_count()}')"
 
       - name: Run multi-GPU training tests
-      # TODO(Steven): Investigate why motors tests are failing in multi-GPU setup
-        run: pytest tests -vv --maxfail=10 --ignore=tests/motors/
+        run: pytest -vv tests/training/

.github/workflows/quality.yml

@@ -50,7 +50,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: '3.12'
 
       - name: Run pre-commit hooks
         uses: pre-commit/action@v3.0.1 # zizmor: ignore[unpinned-uses]

.github/workflows/release.yml

@@ -22,7 +22,7 @@ on:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
 
 jobs:
   # This job builds the Python package and publishes it to PyPI
@@ -45,7 +45,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v6
         with:
-          python-version: '3.10'
+          python-version: '3.12'
 
       - name: Extract Version
         id: extract_info

.github/workflows/unbound_deps_tests.yml

@@ -29,7 +29,7 @@ permissions:
 # Sets up the environment variables
 env:
   UV_VERSION: "0.8.0"
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:unbound
 
 # Ensures that only the latest action is built, canceling older runs.

.pre-commit-config.yaml

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 default_language_version:
-    python: python3.10
+    python: python3.12
 
 exclude: "tests/artifacts/.*\\.safetensors$"
 
@@ -55,7 +55,7 @@ repos:
     rev: v3.21.0
     hooks:
     -   id: pyupgrade
-        args: [--py310-plus]
+        args: [--py312-plus]
 
   ##### Markdown Quality #####
   - repo: https://github.com/rbubley/mirrors-prettier

docker/Dockerfile.internal

@@ -24,7 +24,7 @@ ARG OS_VERSION=22.04
 FROM nvidia/cuda:${CUDA_VERSION}-base-ubuntu${OS_VERSION}
 
 # Define Python version argument
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 
 # Configure environment variables
 ENV DEBIAN_FRONTEND=noninteractive \

docker/Dockerfile.user

@@ -19,7 +19,7 @@
 # docker run -it --rm lerobot-user
 
 # Configure the base image
-ARG PYTHON_VERSION=3.10
+ARG PYTHON_VERSION=3.12
 FROM python:${PYTHON_VERSION}-slim
 
 # Configure environment variables

docs/source/bring_your_own_policies.mdx

@@ -32,7 +32,7 @@ version = "0.1.0"
 dependencies = [
     # your policy-specific dependencies
 ]
-requires-python = ">= 3.11"
+requires-python = ">= 3.12"
 
 [build-system]
 build-backend = # your-build-backend
@@ -82,7 +82,7 @@ Create your policy implementation by inheriting from LeRobot's base `PreTrainedP
 # modeling_my_custom_policy.py
 import torch
 import torch.nn as nn
-from typing import Dict, Any
+from typing import Any
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from .configuration_my_custom_policy import MyCustomPolicyConfig
@@ -91,7 +91,7 @@ class MyCustomPolicy(PreTrainedPolicy):
     config_class = MyCustomPolicyConfig
     name = "my_custom_policy"
 
-    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: Dict[str, Any] = None):
+    def __init__(self, config: MyCustomPolicyConfig, dataset_stats: dict[str, Any] = None):
         super().__init__(config, dataset_stats)
         ...
 ```
@@ -102,7 +102,7 @@ Create processor functions:
 
 ```python
 # processor_my_custom_policy.py
-from typing import Dict, Any
+from typing import Any
 import torch
 
 

docs/source/earthrover_mini_plus.mdx

@@ -13,7 +13,7 @@ The EarthRover Mini Plus is a fully open source mobile robot that connects throu
 ### Hardware
 
 - EarthRover Mini robot
-- Computer with Python 3.10 or newer
+- Computer with Python 3.12 or newer
 - Internet connection
 
 ### Setting Up the Frodobots SDK

docs/source/installation.mdx

@@ -1,6 +1,6 @@
 # Installation
 
-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-).
+This guide uses conda (via miniforge) to manage environments. If you prefer another environment manager (e.g. `uv`, `venv`), ensure you have Python >=3.12 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/)
 
@@ -11,10 +11,10 @@ bash Miniforge3-$(uname)-$(uname -m).sh
 
 ## Step 2: Environment Setup
 
-Create a virtual environment with Python 3.10, using conda:
+Create a virtual environment with Python 3.12, using conda:
 
 ```bash
-conda create -y -n lerobot python=3.10
+conda create -y -n lerobot python=3.12
 ```
 
 Then activate your conda environment, you have to do this each time you open a shell to use lerobot:

docs/source/unitree_g1.mdx

@@ -123,7 +123,7 @@ SSH into the robot and install LeRobot:
 ```bash
 ssh unitree@<YOUR_ROBOT_IP>
 
-conda create -y -n lerobot python=3.10
+conda create -y -n lerobot python=3.12
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot
@@ -153,7 +153,7 @@ With the robot server running, you can now control the robot remotely. Let's lau
 ### Step 1: Install LeRobot on your machine
 
 ```bash
-conda create -y -n lerobot python=3.10
+conda create -y -n lerobot python=3.12
 conda activate lerobot
 git clone https://github.com/huggingface/lerobot.git
 cd lerobot

examples/dataset/slurm_compute_rabc.py

@@ -0,0 +1,490 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+SLURM-distributed SARM RA-BC annotation pipeline.
+
+Computes SARM progress values for all frames in a dataset, distributed across
+SLURM workers, then merges the shards into a single sarm_progress.parquet.
+
+Two subcommands, each a separate SLURM submission:
+
+  compute    – N workers, each computes progress for a subset of episodes
+  aggregate  – 1 worker, merges N shards into sarm_progress.parquet, pushes to hub
+
+Usage:
+    python slurm_compute_rabc.py compute \\
+        --repo-id user/dataset --reward-model-path user/sarm_model \\
+        --stride 10 --device cpu --workers 50 --partition cpu
+
+    python slurm_compute_rabc.py aggregate \\
+        --repo-id user/dataset --reward-model-path user/sarm_model \\
+        --partition cpu --push-to-hub
+"""
+
+import argparse
+from pathlib import Path
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+
+class ComputeProgressShards(PipelineStep):
+    """Each worker computes SARM progress for its assigned episodes."""
+
+    def __init__(
+        self, repo_id, reward_model_path, stride=1, head_mode="sparse", device="cpu", shard_dir="rabc_shards"
+    ):
+        super().__init__()
+        if stride < 1:
+            raise ValueError(f"stride must be >= 1, got {stride}")
+        self.repo_id = repo_id
+        self.reward_model_path = reward_model_path
+        self.stride = stride
+        self.head_mode = head_mode
+        self.device = device
+        self.shard_dir = shard_dir
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        import logging
+        from pathlib import Path
+
+        import numpy as np
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+        import torch
+        from tqdm import tqdm
+
+        from lerobot.policies.sarm.compute_rabc_weights import (
+            generate_all_frame_indices,
+            interpolate_progress,
+            load_sarm_resources,
+        )
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+
+        dataset, reward_model, preprocess = load_sarm_resources(
+            self.repo_id,
+            self.reward_model_path,
+            self.device,
+        )
+
+        if hasattr(preprocess, "eval"):
+            preprocess.eval()
+        for step in preprocess.steps:
+            if hasattr(step, "eval"):
+                step.eval()
+
+        image_key = reward_model.config.image_key
+        state_key = reward_model.config.state_key
+        frame_gap = reward_model.config.frame_gap
+        center_idx = reward_model.config.n_obs_steps // 2
+
+        dual_mode = reward_model.config.uses_dual_heads
+        compute_sparse = self.head_mode in ("sparse", "both") or not dual_mode
+        compute_dense = self.head_mode in ("dense", "both") and dual_mode
+
+        my_episodes = list(range(dataset.num_episodes))[rank::world_size]
+        if not my_episodes:
+            logging.info(f"Rank {rank}: no episodes assigned")
+            return
+        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.num_episodes} episodes")
+
+        all_rows = []
+
+        for ep_idx in tqdm(my_episodes, desc=f"Rank {rank}"):
+            ep = dataset.meta.episodes[ep_idx]
+            ep_start, ep_end = ep["dataset_from_index"], ep["dataset_to_index"]
+            task = dataset[ep_start].get("task", "perform the task")
+
+            all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
+            if self.stride > 1:
+                compute_indices = [i for i in all_ep_indices if (i - ep_start) % self.stride == 0]
+                if (ep_end - 1) not in compute_indices:
+                    compute_indices.append(ep_end - 1)
+                compute_indices = sorted(set(compute_indices))
+            else:
+                compute_indices = all_ep_indices
+
+            frame_results = {}
+            for qi in tqdm(compute_indices, desc=f"  Ep {ep_idx}", leave=False):
+                try:
+                    sample = dataset[qi]
+                    batch = {
+                        image_key: sample[image_key],
+                        "task": task,
+                        "index": qi,
+                        "episode_index": ep_idx,
+                    }
+                    if state_key in sample:
+                        batch[state_key] = sample[state_key]
+
+                    with torch.no_grad():
+                        processed = preprocess(batch)
+                        vf = processed["video_features"].to(self.device)
+                        tf = processed["text_features"].to(self.device)
+                        sf = processed.get("state_features")
+                        if sf is not None:
+                            sf = sf.to(self.device)
+                        lengths = processed.get("lengths")
+
+                        sparse_val = dense_val = np.nan
+                        if compute_sparse:
+                            r = reward_model.calculate_rewards(
+                                text_embeddings=tf,
+                                video_embeddings=vf,
+                                state_features=sf,
+                                lengths=lengths,
+                                return_all_frames=True,
+                                head_mode="sparse",
+                            )
+                            sparse_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
+                        if compute_dense:
+                            r = reward_model.calculate_rewards(
+                                text_embeddings=tf,
+                                video_embeddings=vf,
+                                state_features=sf,
+                                lengths=lengths,
+                                return_all_frames=True,
+                                head_mode="dense",
+                            )
+                            dense_val = float(r[0, center_idx] if r.ndim == 2 else r[center_idx])
+
+                        frame_results[qi] = (sparse_val, dense_val)
+                except Exception as e:
+                    logging.warning(f"Failed frame {qi}: {e}")
+
+            if not frame_results:
+                logging.warning(f"Episode {ep_idx}: all frames failed, skipping")
+                continue
+
+            # Interpolate to all frames in this episode
+            computed_idx = np.array(sorted(frame_results.keys()))
+            all_frame_arr = np.arange(ep_start, ep_end)
+
+            sparse_vals = np.array([frame_results[i][0] for i in computed_idx]) if compute_sparse else None
+            dense_vals = np.array([frame_results[i][1] for i in computed_idx]) if compute_dense else None
+
+            if self.stride > 1 and len(computed_idx) > 1:
+                if compute_sparse:
+                    sparse_vals = interpolate_progress(computed_idx, sparse_vals, all_frame_arr)
+                if compute_dense:
+                    dense_vals = interpolate_progress(computed_idx, dense_vals, all_frame_arr)
+                output_frames = all_frame_arr
+            else:
+                # Use only successfully computed frames to avoid indexing mismatch on failures
+                output_frames = computed_idx
+
+            for i, fi in enumerate(output_frames):
+                row = {"index": int(fi), "episode_index": ep_idx, "frame_index": int(fi - ep_start)}
+                if compute_sparse:
+                    row["progress_sparse"] = float(sparse_vals[i])
+                if compute_dense:
+                    row["progress_dense"] = float(dense_vals[i])
+                all_rows.append(row)
+
+        if all_rows:
+            import pandas as pd
+
+            df = pd.DataFrame(all_rows).sort_values("index").reset_index(drop=True)
+            table = pa.Table.from_pandas(df, preserve_index=False)
+            table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
+            shard_dir = Path(self.shard_dir)
+            shard_dir.mkdir(parents=True, exist_ok=True)
+            out = shard_dir / f"shard_{rank:05d}.parquet"
+            pq.write_table(table, out)
+            logging.info(f"Rank {rank}: saved {len(df)} rows to {out}")
+
+
+class AggregateProgress(PipelineStep):
+    """Merge all shard parquets into final sarm_progress.parquet."""
+
+    def __init__(self, repo_id, reward_model_path, shard_dir="rabc_shards", push_to_hub=False):
+        super().__init__()
+        self.repo_id = repo_id
+        self.reward_model_path = reward_model_path
+        self.shard_dir = shard_dir
+        self.push_to_hub = push_to_hub
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        import datetime
+        import logging
+        import os
+        from pathlib import Path
+
+        import pandas as pd
+        import pyarrow as pa
+        import pyarrow.parquet as pq
+
+        from lerobot.datasets.lerobot_dataset import LeRobotDataset
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+        if rank != 0:
+            return
+
+        shard_dir = Path(self.shard_dir)
+        shards = sorted(shard_dir.glob("shard_*.parquet"))
+        if not shards:
+            raise FileNotFoundError(f"No shards found in {shard_dir}")
+
+        # Log shard modification time range to help detect stale files
+        mtimes = [os.path.getmtime(s) for s in shards]
+        oldest = datetime.datetime.fromtimestamp(min(mtimes)).isoformat(timespec="seconds")
+        newest = datetime.datetime.fromtimestamp(max(mtimes)).isoformat(timespec="seconds")
+        logging.info(f"Aggregating {len(shards)} shards (oldest: {oldest}, newest: {newest})")
+
+        df = pd.concat([pd.read_parquet(s) for s in shards], ignore_index=True)
+        df = df.sort_values("index").reset_index(drop=True)
+
+        table = pa.Table.from_pandas(df, preserve_index=False)
+        table = table.replace_schema_metadata({b"reward_model_path": self.reward_model_path.encode()})
+
+        temp_ds = LeRobotDataset(self.repo_id, download_videos=False)
+        out_path = Path(temp_ds.root) / "sarm_progress.parquet"
+        out_path.parent.mkdir(parents=True, exist_ok=True)
+        pq.write_table(table, out_path)
+        logging.info(f"Saved {len(df)} rows to {out_path}")
+
+        for col in ["progress_sparse", "progress_dense"]:
+            if col in df.columns:
+                v = df[col].dropna()
+                logging.info(
+                    f"{col}: mean={v.mean():.4f} std={v.std():.4f} min={v.min():.4f} max={v.max():.4f}"
+                )
+
+        if self.push_to_hub:
+            from huggingface_hub import HfApi
+
+            api = HfApi()
+            hub_path = "sarm_progress.parquet"
+            logging.info(f"Uploading to {self.repo_id}/{hub_path}")
+            api.upload_file(
+                path_or_fileobj=str(out_path),
+                path_in_repo=hub_path,
+                repo_id=self.repo_id,
+                repo_type="dataset",
+            )
+            logging.info(f"Uploaded: https://huggingface.co/datasets/{self.repo_id}/blob/main/{hub_path}")
+
+
+def make_compute_executor(
+    repo_id,
+    reward_model_path,
+    stride,
+    head_mode,
+    device,
+    shard_dir,
+    logs_dir,
+    job_name,
+    slurm,
+    workers,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+):
+    kwargs = {
+        "pipeline": [
+            ComputeProgressShards(repo_id, reward_model_path, stride, head_mode, device, str(shard_dir)),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": workers,
+                "workers": workers,
+                "time": "24:00:00",
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": workers, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def make_aggregate_executor(
+    repo_id,
+    reward_model_path,
+    shard_dir,
+    logs_dir,
+    job_name,
+    slurm,
+    partition,
+    cpus_per_task,
+    mem_per_cpu,
+    push_to_hub,
+):
+    kwargs = {
+        "pipeline": [
+            AggregateProgress(repo_id, reward_model_path, str(shard_dir), push_to_hub),
+        ],
+        "logging_dir": str(logs_dir / job_name),
+    }
+
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": 1,
+                "workers": 1,
+                "time": "02:00:00",
+                "partition": partition,
+                "cpus_per_task": cpus_per_task,
+                "sbatch_args": {"mem-per-cpu": mem_per_cpu},
+            }
+        )
+        return SlurmPipelineExecutor(**kwargs)
+
+    kwargs.update({"tasks": 1, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def _add_shared_args(p):
+    p.add_argument(
+        "--repo-id",
+        type=str,
+        required=True,
+        help="Hugging Face repository identifier, e.g. 'user/dataset'.",
+    )
+    p.add_argument(
+        "--shard-dir",
+        type=Path,
+        default=Path("rabc_shards"),
+        help="Directory to read/write per-rank parquet shards.",
+    )
+    p.add_argument(
+        "--logs-dir",
+        type=Path,
+        default=Path("logs"),
+        help="Directory for datatrove logs.",
+    )
+    p.add_argument(
+        "--job-name",
+        type=str,
+        default=None,
+        help="SLURM job name (defaults to rabc_<subcommand>).",
+    )
+    p.add_argument(
+        "--slurm",
+        type=int,
+        default=1,
+        help="1 = submit via SLURM; 0 = run locally (useful for debugging).",
+    )
+    p.add_argument(
+        "--partition",
+        type=str,
+        default=None,
+        help="SLURM partition to submit to.",
+    )
+    p.add_argument(
+        "--cpus-per-task",
+        type=int,
+        default=4,
+        help="Number of CPUs per SLURM task.",
+    )
+    p.add_argument(
+        "--mem-per-cpu",
+        type=str,
+        default="4G",
+        help="Memory per CPU, e.g. '4G' or '1950M'.",
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="SLURM-distributed SARM RA-BC annotation pipeline",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    # compute subcommand
+    cp = sub.add_parser(
+        "compute",
+        help="Distribute progress computation across SLURM workers.",
+    )
+    _add_shared_args(cp)
+    cp.add_argument(
+        "--reward-model-path",
+        type=str,
+        required=True,
+        help="Path or HF repo id of the SARM reward model.",
+    )
+    cp.add_argument(
+        "--stride",
+        type=int,
+        default=1,
+        help="Compute every Nth frame; intermediate frames are interpolated (must be >= 1).",
+    )
+    cp.add_argument(
+        "--head-mode",
+        type=str,
+        default="sparse",
+        choices=["sparse", "dense", "both"],
+        help="Which reward head(s) to compute.",
+    )
+    cp.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="Device for reward model inference, e.g. 'cpu' or 'cuda'.",
+    )
+    cp.add_argument(
+        "--workers",
+        type=int,
+        default=50,
+        help="Number of parallel SLURM tasks (one shard per worker).",
+    )
+
+    # aggregate subcommand
+    ap = sub.add_parser(
+        "aggregate",
+        help="Merge per-rank shards into a single sarm_progress.parquet.",
+    )
+    _add_shared_args(ap)
+    ap.add_argument(
+        "--reward-model-path",
+        type=str,
+        required=True,
+        help="Path or HF repo id of the SARM reward model (stored in parquet metadata).",
+    )
+    ap.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Upload sarm_progress.parquet to the Hugging Face Hub after aggregation.",
+    )
+
+    args = parser.parse_args()
+    job_name = args.job_name or f"rabc_{args.command}"
+    kwargs = vars(args)
+    kwargs["slurm"] = kwargs.pop("slurm") == 1
+    kwargs["job_name"] = job_name
+    command = kwargs.pop("command")
+
+    executor = make_compute_executor(**kwargs) if command == "compute" else make_aggregate_executor(**kwargs)
+
+    executor.run()
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -29,7 +29,7 @@ version = "0.4.5"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 authors = [
     { name = "Rémi Cadène", email = "re.cadene@gmail.com" },
     { name = "Simon Alibert", email = "alibert.sim@gmail.com" },
@@ -50,7 +50,8 @@ classifiers = [
     "Intended Audience :: Education",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Apache Software License",
-    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
     "Topic :: Software Development :: Build Tools",
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
@@ -61,26 +62,28 @@ dependencies = [
     # Hugging Face dependencies
     "datasets>=4.0.0,<5.0.0",
     "diffusers>=0.27.2,<0.36.0",
-    "huggingface-hub[cli]>=1.0.0,<2.0.0",
+    "huggingface-hub>=1.0.0,<2.0.0",
     "accelerate>=1.10.0,<2.0.0",
 
     # Core dependencies
+    "numpy>=2.0.0,<2.3.0", # NOTE: Explicitly listing numpy helps the resolver converge faster. Upper bound imposed by opencv-python-headless.
     "setuptools>=71.0.0,<81.0.0",
     "cmake>=3.29.0.1,<4.2.0",
+    "packaging>=24.2,<26.0",
+
+    "torch>=2.2.1,<2.11.0",
+    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')",
+    "torchvision>=0.21.0,<0.26.0",
+
     "einops>=0.8.0,<0.9.0",
     "opencv-python-headless>=4.9.0,<4.13.0",
     "av>=15.0.0,<16.0.0",
     "jsonlines>=4.0.0,<5.0.0",
-    "packaging>=24.2,<26.0",
-    "pynput>=1.7.7,<1.9.0",
+    "pynput>=1.7.8,<1.9.0",
     "pyserial>=3.5,<4.0",
+
     "wandb>=0.24.0,<0.25.0",
-
-    "torch>=2.2.1,<2.11.0", # TODO: Bump dependency
-    "torchcodec>=0.2.1,<0.11.0; sys_platform != 'win32' and (sys_platform != 'linux' or (platform_machine != 'aarch64' and platform_machine != 'arm64' and platform_machine != 'armv7l')) and (sys_platform != 'darwin' or platform_machine != 'x86_64')", # TODO: Bump dependency
-    "torchvision>=0.21.0,<0.26.0", # TODO: Bump dependency
-
-    "draccus==0.10.0", # TODO: Remove ==
+    "draccus==0.10.0", # TODO: Relax version constraint
     "gymnasium>=1.1.1,<2.0.0",
     "rerun-sdk>=0.24.0,<0.27.0",
 
@@ -95,13 +98,14 @@ dependencies = [
 
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
-placo-dep = ["placo>=0.9.6,<0.10.0"]
+placo-dep = ["placo>=0.9.6,<0.9.17"]
 transformers-dep = ["transformers>=5.3.0,<6.0.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf>=6.31.1,<6.32.0"]
 can-dep = ["python-can>=4.2.0,<5.0.0"]
 peft-dep = ["peft>=0.18.0,<1.0.0"]
 scipy-dep = ["scipy>=1.14.0,<2.0.0"]
 qwen-vl-utils-dep = ["qwen-vl-utils>=0.0.11,<0.1.0"]
+matplotlib-dep = ["matplotlib>=3.10.3,<4.0.0", "contourpy>=1.3.0,<2.0.0"] # NOTE: Explicitly listing contourpy helps the resolver converge faster.
 
 # Motors
 feetech = ["feetech-servo-sdk>=1.0.0,<2.0.0"]
@@ -119,16 +123,16 @@ unitree_g1 = [
     "onnxruntime>=1.16.0,<2.0.0",
     "pin>=3.0.0,<4.0.0",
     "meshcat>=0.3.0,<0.4.0",
-    "matplotlib>=3.9.0,<4.0.0",
+    "lerobot[matplotlib-dep]",
     "casadi>=3.6.0,<4.0.0",
 ]
 reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"]
 kinematics = ["lerobot[placo-dep]"]
 intelrealsense = [
     "pyrealsense2>=2.55.1.6486,<2.57.0 ; sys_platform != 'darwin'",
-    "pyrealsense2-macosx>=2.54,<2.55.0 ; sys_platform == 'darwin'",
+    "pyrealsense2-macosx>=2.54,<2.57.0 ; sys_platform == 'darwin'",
 ]
-phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]
+phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0", "lerobot[scipy-dep]"]
 
 # Policies
 wallx = [
@@ -151,12 +155,12 @@ groot = [
     "ninja>=1.11.1,<2.0.0",
     "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
-sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "lerobot[qwen-vl-utils-dep]"]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "lerobot[matplotlib-dep]", "lerobot[qwen-vl-utils-dep]"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]
 
 # Features
-async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
+async = ["lerobot[grpcio-dep]", "lerobot[matplotlib-dep]"]
 peft = ["lerobot[transformers-dep]", "lerobot[peft-dep]"]
 
 # Development
@@ -165,13 +169,19 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
-aloha = ["gym-aloha>=0.1.2,<0.2.0"]
+# NOTE: Explicitly listing scipy helps flatten the dependecy tree.
+aloha = ["gym-aloha>=0.1.2,<0.2.0", "lerobot[scipy-dep]"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0"]
-metaworld = ["metaworld==3.0.0"]
+libero = ["lerobot[transformers-dep]", "hf-libero>=0.1.3,<0.2.0; sys_platform == 'linux'", "lerobot[scipy-dep]"]
+metaworld = ["metaworld==3.0.0", "lerobot[scipy-dep]"]
 
 # All
 all = [
+    # NOTE(resolver hint): scipy is pulled in transitively via lerobot[scipy-dep] through
+    # multiple extras (aloha, metaworld, pi, wallx, phone). Listing it explicitly
+    # helps pip's resolver converge by constraining scipy early, before it encounters
+    # the loose scipy requirements from transitive deps like dm-control and metaworld.
+    "scipy>=1.14.0,<2.0.0",
     "lerobot[dynamixel]",
     "lerobot[gamepad]",
     "lerobot[hopejr]",
@@ -192,7 +202,7 @@ all = [
     "lerobot[aloha]",
     "lerobot[pusht]",
     "lerobot[phone]",
-    "lerobot[libero]",
+    "lerobot[libero]; sys_platform == 'linux'",
     "lerobot[metaworld]",
     "lerobot[sarm]",
     "lerobot[peft]",
@@ -224,7 +234,7 @@ lerobot = ["envs/*.json"]
 where = ["src"]
 
 [tool.ruff]
-target-version = "py310"
+target-version = "py312"
 line-length = 110
 exclude = ["tests/artifacts/**/*.safetensors", "*_pb2.py", "*_pb2_grpc.py"]
 
@@ -316,7 +326,7 @@ default.extend-ignore-identifiers-re = [
 # Uncomment [tool.mypy] first, then uncomment individual module overrides as they get proper type annotations
 
 [tool.mypy]
-python_version = "3.10"
+python_version = "3.12"
 ignore_missing_imports = true
 follow_imports = "skip"
 # warn_return_any = true

src/lerobot/datasets/dataset_tools.py

@@ -25,6 +25,7 @@ This module provides utilities for:
 
 import logging
 import shutil
+from collections import defaultdict
 from collections.abc import Callable
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
@@ -45,6 +46,8 @@ from lerobot.datasets.utils import (
     DEFAULT_DATA_FILE_SIZE_IN_MB,
     DEFAULT_DATA_PATH,
     DEFAULT_EPISODES_PATH,
+    DEFAULT_SUBTASKS_PATH,
+    flatten_dict,
     get_parquet_file_size_in_mb,
     load_episodes,
     update_chunk_file_indices,
@@ -141,6 +144,315 @@ def delete_episodes(
     return new_dataset
 
 
+def trim_episode_start(
+    dataset: LeRobotDataset,
+    seconds: float,
+    episode_indices: list[int] | None = None,
+    output_dir: str | Path | None = None,
+    repo_id: str | None = None,
+) -> LeRobotDataset:
+    """Trim the first N seconds from selected episodes and create a new dataset.
+
+    The operation rewrites data parquet files and updates episode metadata so that:
+    - frame_index starts at 0 for each trimmed episode
+    - timestamp starts at 0 for each trimmed episode
+    - global index remains contiguous across the full dataset
+    - dataset_from_index / dataset_to_index reflect new frame ranges
+
+    Video files are copied as-is and per-episode video timestamps are shifted forward
+    for trimmed episodes.
+
+    Episodes selected for trimming that are too short (length <= trim_frames) are skipped
+    from the output dataset.
+
+    Args:
+        dataset: The source LeRobotDataset.
+        seconds: Number of seconds to remove from episode starts.
+        episode_indices: Optional list of episode indices to trim. If None, trims all episodes.
+        output_dir: Directory to save the new dataset. If None, uses default location.
+        repo_id: Repository ID for the new dataset. If None, appends "_trimmed" to original.
+    """
+    if seconds <= 0:
+        raise ValueError(f"seconds must be strictly positive, got {seconds}")
+
+    if dataset.meta.episodes is None:
+        dataset.meta.episodes = load_episodes(dataset.meta.root)
+
+    trim_frames = int(seconds * dataset.meta.fps)
+    if trim_frames <= 0:
+        raise ValueError(
+            f"seconds={seconds} corresponds to 0 frames at fps={dataset.meta.fps}. "
+            "Increase seconds so at least one frame is trimmed."
+        )
+
+    if episode_indices is None:
+        episode_indices = list(range(dataset.meta.total_episodes))
+
+    if len(episode_indices) == 0:
+        raise ValueError("No episodes specified to trim")
+
+    episode_indices = sorted(set(episode_indices))
+    valid_indices = set(range(dataset.meta.total_episodes))
+    invalid = set(episode_indices) - valid_indices
+    if invalid:
+        raise ValueError(f"Invalid episode indices: {invalid}")
+
+    too_short = sorted(
+        ep_idx for ep_idx in episode_indices if int(dataset.meta.episodes[ep_idx]["length"]) <= trim_frames
+    )
+    trim_set = set(episode_indices)
+    skipped_set = set(too_short)
+    trim_set -= skipped_set
+
+    if too_short:
+        logging.warning(
+            f"Skipping {len(too_short)} episode(s) that are too short to trim "
+            f"({trim_frames} frames): {too_short}"
+        )
+
+    episodes_to_keep = [ep_idx for ep_idx in range(dataset.meta.total_episodes) if ep_idx not in skipped_set]
+    if not episodes_to_keep:
+        raise ValueError(
+            "All episodes selected for trimming are too short and would be skipped. "
+            "Try a smaller trim duration."
+        )
+
+    logging.info(
+        f"Trimming {len(trim_set)} episode(s) by {seconds}s and keeping {len(episodes_to_keep)} "
+        f"episode(s) in output"
+    )
+
+    if repo_id is None:
+        repo_id = f"{dataset.repo_id}_trimmed"
+    output_dir = Path(output_dir) if output_dir is not None else HF_LEROBOT_HOME / repo_id
+
+    new_meta = LeRobotDatasetMetadata.create(
+        repo_id=repo_id,
+        fps=dataset.meta.fps,
+        features=dataset.meta.features,
+        robot_type=dataset.meta.robot_type,
+        root=output_dir,
+        use_videos=len(dataset.meta.video_keys) > 0,
+        chunks_size=dataset.meta.chunks_size,
+        data_files_size_in_mb=dataset.meta.data_files_size_in_mb,
+        video_files_size_in_mb=dataset.meta.video_files_size_in_mb,
+    )
+
+    if dataset.meta.tasks is not None:
+        write_tasks(dataset.meta.tasks, new_meta.root)
+        new_meta.tasks = dataset.meta.tasks.copy()
+
+    subtasks_path = dataset.root / DEFAULT_SUBTASKS_PATH
+    if subtasks_path.exists():
+        dst_subtasks_path = new_meta.root / DEFAULT_SUBTASKS_PATH
+        dst_subtasks_path.parent.mkdir(parents=True, exist_ok=True)
+        shutil.copy(subtasks_path, dst_subtasks_path)
+
+    episode_mapping = {old_idx: new_idx for new_idx, old_idx in enumerate(episodes_to_keep)}
+    trim_duration_s = trim_frames / dataset.meta.fps
+
+    episode_lengths: dict[int, int] = {}
+    episode_ranges: dict[int, tuple[int, int]] = {}
+    total_frames = 0
+    for old_ep_idx in episodes_to_keep:
+        new_ep_idx = episode_mapping[old_ep_idx]
+        src_length = int(dataset.meta.episodes[old_ep_idx]["length"])
+        new_length = src_length - trim_frames if old_ep_idx in trim_set else src_length
+        episode_lengths[new_ep_idx] = new_length
+        episode_ranges[new_ep_idx] = (total_frames, total_frames + new_length)
+        total_frames += new_length
+
+    numeric_features = {
+        k: v
+        for k, v in dataset.meta.features.items()
+        if v["dtype"] not in ["image", "video", "string"]
+    }
+    episode_stats_parts: dict[int, list[dict[str, dict]]] = defaultdict(list)
+    episode_file_metadata: dict[int, dict[str, int]] = {}
+
+    data_dir = dataset.root / DATA_DIR
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    if not parquet_files:
+        raise ValueError(f"No parquet files found in {data_dir}")
+
+    for src_path in tqdm(parquet_files, desc="Trimming data files"):
+        df = pd.read_parquet(src_path).reset_index(drop=True)
+
+        if len(df) == 0:
+            continue
+
+        if skipped_set:
+            keep_mask = ~df["episode_index"].isin(skipped_set)
+            if not keep_mask.all():
+                df = df.loc[keep_mask].copy().reset_index(drop=True)
+
+        if len(df) == 0:
+            continue
+
+        if trim_set:
+            trim_mask = df["episode_index"].isin(trim_set) & (df["frame_index"] < trim_frames)
+            if trim_mask.any():
+                df = df.loc[~trim_mask].copy().reset_index(drop=True)
+
+        if len(df) == 0:
+            continue
+
+        relative_path = src_path.relative_to(dataset.root)
+        chunk_idx = int(relative_path.parts[1].split("-")[1])
+        file_idx = int(relative_path.parts[2].split("-")[1].split(".")[0])
+
+        for old_ep_idx in sorted(df["episode_index"].unique().tolist()):
+            ep_mask = df["episode_index"] == old_ep_idx
+            new_ep_idx = episode_mapping[old_ep_idx]
+
+            if old_ep_idx in trim_set:
+                df.loc[ep_mask, "frame_index"] = df.loc[ep_mask, "frame_index"] - trim_frames
+                shifted_timestamps = df.loc[ep_mask, "timestamp"].to_numpy(dtype=np.float64) - trim_duration_s
+                df.loc[ep_mask, "timestamp"] = np.clip(shifted_timestamps, a_min=0.0, a_max=None)
+
+            df.loc[ep_mask, "episode_index"] = new_ep_idx
+
+            ep_start, _ = episode_ranges[new_ep_idx]
+            new_indices = ep_start + df.loc[ep_mask, "frame_index"].to_numpy(dtype=np.int64)
+            df.loc[ep_mask, "index"] = new_indices
+
+            if new_ep_idx in episode_file_metadata:
+                existing = episode_file_metadata[new_ep_idx]
+                if (
+                    existing["data/chunk_index"] != chunk_idx
+                    or existing["data/file_index"] != file_idx
+                ):
+                    raise ValueError(
+                        f"Episode {old_ep_idx} spans multiple data files. "
+                        "trim_episode_start currently expects one data file per episode."
+                    )
+            else:
+                episode_file_metadata[new_ep_idx] = {
+                    "data/chunk_index": chunk_idx,
+                    "data/file_index": file_idx,
+                }
+
+            if numeric_features:
+                ep_df = df.loc[ep_mask]
+                episode_data: dict[str, np.ndarray] = {}
+                episode_feature_spec: dict[str, dict] = {}
+
+                for key, feature in numeric_features.items():
+                    if key not in ep_df.columns:
+                        continue
+
+                    values = ep_df[key].to_numpy()
+                    if len(values) == 0:
+                        continue
+
+                    first_value = values[0]
+                    if isinstance(first_value, np.ndarray):
+                        episode_data[key] = np.stack(values)
+                    elif isinstance(first_value, (list, tuple)):
+                        episode_data[key] = np.stack(values)
+                    else:
+                        episode_data[key] = np.asarray(values)
+
+                    episode_feature_spec[key] = feature
+
+                if episode_data:
+                    episode_stats_parts[new_ep_idx].append(
+                        compute_episode_stats(episode_data, episode_feature_spec)
+                    )
+
+        df["index"] = df["index"].astype(np.int64)
+        if "frame_index" in df.columns:
+            df["frame_index"] = df["frame_index"].astype(np.int64)
+
+        dst_path = new_meta.root / DEFAULT_DATA_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+        dst_path.parent.mkdir(parents=True, exist_ok=True)
+        _write_parquet(df, dst_path, new_meta)
+
+    all_episode_stats = []
+    for old_ep_idx in tqdm(episodes_to_keep, desc="Writing episode metadata"):
+        new_ep_idx = episode_mapping[old_ep_idx]
+
+        if new_ep_idx not in episode_file_metadata:
+            raise ValueError(f"Missing data file metadata for episode {old_ep_idx}")
+
+        from_idx, to_idx = episode_ranges[new_ep_idx]
+        src_episode = dataset.meta.episodes[old_ep_idx]
+        ep_data_meta = episode_file_metadata[new_ep_idx]
+
+        stats_parts = episode_stats_parts.get(new_ep_idx, [])
+        ep_stats = aggregate_stats(stats_parts) if len(stats_parts) > 1 else (stats_parts[0] if stats_parts else {})
+        if ep_stats:
+            all_episode_stats.append(ep_stats)
+
+        episode_meta = {
+            "data/chunk_index": ep_data_meta["data/chunk_index"],
+            "data/file_index": ep_data_meta["data/file_index"],
+            "dataset_from_index": from_idx,
+            "dataset_to_index": to_idx,
+        }
+
+        for video_key in dataset.meta.video_keys:
+            from_ts = src_episode[f"videos/{video_key}/from_timestamp"]
+            if old_ep_idx in trim_set:
+                from_ts += trim_duration_s
+            episode_meta.update(
+                {
+                    f"videos/{video_key}/chunk_index": src_episode[f"videos/{video_key}/chunk_index"],
+                    f"videos/{video_key}/file_index": src_episode[f"videos/{video_key}/file_index"],
+                    f"videos/{video_key}/from_timestamp": from_ts,
+                    f"videos/{video_key}/to_timestamp": src_episode[f"videos/{video_key}/to_timestamp"],
+                }
+            )
+
+        episode_dict = {
+            "episode_index": new_ep_idx,
+            "tasks": src_episode["tasks"],
+            "length": episode_lengths[new_ep_idx],
+        }
+        episode_dict.update(episode_meta)
+        if ep_stats:
+            episode_dict.update(flatten_dict({"stats": ep_stats}))
+
+        new_meta._save_episode_metadata(episode_dict)
+
+    new_meta._close_writer()
+
+    if new_meta.video_keys:
+        _copy_videos(dataset, new_meta)
+
+    new_meta.info.update(
+        {
+            "total_episodes": len(episodes_to_keep),
+            "total_frames": total_frames,
+            "total_tasks": len(new_meta.tasks) if new_meta.tasks is not None else 0,
+            "splits": {"train": f"0:{len(episodes_to_keep)}"},
+        }
+    )
+
+    if new_meta.video_keys and dataset.meta.video_keys:
+        for key in new_meta.video_keys:
+            if key in dataset.meta.features:
+                new_meta.info["features"][key]["info"] = dataset.meta.info["features"][key].get("info", {})
+
+    write_info(new_meta.info, new_meta.root)
+
+    merged_stats = aggregate_stats(all_episode_stats) if all_episode_stats else {}
+    if dataset.meta.stats:
+        for key, value in dataset.meta.stats.items():
+            if key not in merged_stats:
+                merged_stats[key] = value
+    if merged_stats:
+        write_stats(merged_stats, new_meta.root)
+
+    return LeRobotDataset(
+        repo_id=repo_id,
+        root=output_dir,
+        image_transforms=dataset.image_transforms,
+        delta_timestamps=dataset.delta_timestamps,
+        tolerance_s=dataset.tolerance_s,
+    )
+
+
 def split_dataset(
     dataset: LeRobotDataset,
     splits: dict[str, float | list[int]],

src/lerobot/datasets/utils.py

@@ -21,7 +21,7 @@ from collections import deque
 from collections.abc import Iterable, Iterator
 from pathlib import Path
 from pprint import pformat
-from typing import Any, Generic, TypeVar
+from typing import Any
 
 import datasets
 import numpy as np
@@ -78,8 +78,6 @@ DEFAULT_FEATURES = {
     "task_index": {"dtype": "int64", "shape": (1,), "names": None},
 }
 
-T = TypeVar("T")
-
 
 def get_parquet_file_size_in_mb(parquet_path: str | Path) -> float:
     metadata = pq.read_metadata(parquet_path)
@@ -1234,7 +1232,7 @@ class LookAheadError(Exception):
     pass
 
 
-class Backtrackable(Generic[T]):
+class Backtrackable[T]:
     """
     Wrap any iterator/iterable so you can step back up to `history` items
     and look ahead up to `lookahead` items.

src/lerobot/datasets/v30/convert_dataset_v21_to_v30.py

@@ -228,7 +228,6 @@ def convert_data(root: Path, new_root: Path, data_file_size_in_mb: int):
 
             # Reset for the next file
             size_in_mb = 0
-            num_frames += ep_num_frames  # Still need to accumulate total frames
             paths_to_cat = []
 
         # Now create metadata with correct chunk/file indices

src/lerobot/motors/motors_bus.py

@@ -29,7 +29,7 @@ from dataclasses import dataclass
 from enum import Enum
 from functools import cached_property
 from pprint import pformat
-from typing import Protocol, TypeAlias
+from typing import Protocol
 
 import serial
 from deepdiff import DeepDiff
@@ -38,8 +38,8 @@ from tqdm import tqdm
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 from lerobot.utils.utils import enter_pressed, move_cursor_up
 
-NameOrID: TypeAlias = str | int
-Value: TypeAlias = int | float
+type NameOrID = str | int
+type Value = int | float
 
 logger = logging.getLogger(__name__)
 
@@ -1277,4 +1277,4 @@ class SerialMotorsBus(MotorsBusBase):
 
 
 # Backward compatibility alias
-MotorsBus: TypeAlias = SerialMotorsBus
+MotorsBus = SerialMotorsBus

src/lerobot/policies/factory.py

@@ -18,10 +18,9 @@ from __future__ import annotations
 
 import importlib
 import logging
-from typing import Any, TypedDict
+from typing import Any, TypedDict, Unpack
 
 import torch
-from typing_extensions import Unpack
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import FeatureType

src/lerobot/policies/groot/eagle2_hg_model/image_processing_eagle2_5_vl_fast.py

@@ -4,10 +4,9 @@
 # Licensed under The MIT License [see LICENSE for details]
 # --------------------------------------------------------
 
+from __future__ import annotations
 
 # copy from https://github.com/huggingface/transformers/blob/main/src/transformers/models/llava_onevision/image_processing_llava_onevision_fast.py
-from typing import Optional
-
 from transformers.image_processing_utils import (
     BatchFeature,
     get_patch_output_size,
@@ -165,11 +164,11 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _resize_for_patching(
         self,
-        image: "torch.Tensor",
+        image: torch.Tensor,
         target_resolution: tuple,
-        interpolation: "F.InterpolationMode",
+        interpolation: F.InterpolationMode,
         input_data_format: ChannelDimension,
-    ) -> "torch.Tensor":
+    ) -> torch.Tensor:
         """
         Resizes an image to a target resolution while maintaining aspect ratio.
 
@@ -219,8 +218,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
         return best_ratio
 
     def _pad_for_patching(
-        self, image: "torch.Tensor", target_resolution: tuple, input_data_format: ChannelDimension
-    ) -> "torch.Tensor":
+        self, image: torch.Tensor, target_resolution: tuple, input_data_format: ChannelDimension
+    ) -> torch.Tensor:
         """
         Pad an image to a target resolution while maintaining aspect ratio.
         """
@@ -236,15 +235,15 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _get_image_patches(
         self,
-        image: "torch.Tensor",
+        image: torch.Tensor,
         min_num: int,
         max_num: int,
         size: tuple,
         tile_size: int,
         use_thumbnail: bool,
-        interpolation: "F.InterpolationMode",
+        interpolation: F.InterpolationMode,
         pad_during_tiling: bool,
-    ) -> list["torch.Tensor"]:
+    ) -> list[torch.Tensor]:
         image_size = get_image_size(image, channel_dim=ChannelDimension.FIRST)
         orig_height, orig_width = image_size
         aspect_ratio = orig_width / orig_height
@@ -305,8 +304,8 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _pad_for_batching(
         self,
-        pixel_values: list["torch.Tensor"],
-    ) -> list["torch.Tensor"]:
+        pixel_values: list[torch.Tensor],
+    ) -> list[torch.Tensor]:
         """
         Pads images on the `num_of_patches` dimension with zeros to form a batch of same number of patches.
 
@@ -327,14 +326,14 @@ class Eagle25VLImageProcessorFast(BaseImageProcessorFast):
 
     def _preprocess(
         self,
-        images: list["torch.Tensor"],
+        images: list[torch.Tensor],
         do_resize: bool,
         size: SizeDict,
         max_dynamic_tiles: int,
         min_dynamic_tiles: int,
         use_thumbnail: bool,
         pad_during_tiling: bool,
-        interpolation: Optional["F.InterpolationMode"],
+        interpolation: F.InterpolationMode | None,
         do_center_crop: bool,
         crop_size: SizeDict,
         do_rescale: bool,

src/lerobot/policies/pi0/modeling_pi0.py

@@ -20,12 +20,11 @@ import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _transformers_available
 

src/lerobot/policies/pi05/modeling_pi05.py

@@ -20,12 +20,11 @@ import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _transformers_available
 

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -19,13 +19,12 @@ import logging
 import math
 from collections import deque
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal, TypedDict
+from typing import TYPE_CHECKING, Literal, TypedDict, Unpack
 
 import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.utils.import_utils import _scipy_available, _transformers_available
 

src/lerobot/policies/pretrained.py

@@ -19,7 +19,7 @@ import os
 from importlib.resources import files
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TypedDict, TypeVar
+from typing import TypedDict, TypeVar, Unpack
 
 import packaging
 import safetensors
@@ -28,7 +28,6 @@ from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from huggingface_hub.errors import HfHubHTTPError
 from safetensors.torch import load_model as load_model_as_safetensor, save_model as save_model_as_safetensor
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.train import TrainPipelineConfig

src/lerobot/policies/smolvla/modeling_smolvla.py

@@ -54,12 +54,11 @@ policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
 
 import math
 from collections import deque
-from typing import TypedDict
+from typing import TypedDict, Unpack
 
 import torch
 import torch.nn.functional as F  # noqa: N812
 from torch import Tensor, nn
-from typing_extensions import Unpack
 
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor

src/lerobot/processor/core.py

@@ -17,7 +17,7 @@
 from __future__ import annotations
 
 from enum import Enum
-from typing import Any, TypeAlias, TypedDict
+from typing import Any, TypedDict
 
 import numpy as np
 import torch
@@ -36,10 +36,10 @@ class TransitionKey(str, Enum):
     COMPLEMENTARY_DATA = "complementary_data"
 
 
-PolicyAction: TypeAlias = torch.Tensor
-RobotAction: TypeAlias = dict[str, Any]
-EnvAction: TypeAlias = np.ndarray
-RobotObservation: TypeAlias = dict[str, Any]
+PolicyAction = torch.Tensor
+RobotAction = dict[str, Any]
+EnvAction = np.ndarray
+RobotObservation = dict[str, Any]
 
 
 EnvTransition = TypedDict(

src/lerobot/processor/pipeline.py

@@ -39,7 +39,7 @@ from collections.abc import Callable, Iterable, Sequence
 from copy import deepcopy
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Generic, TypeAlias, TypedDict, TypeVar, cast
+from typing import Any, TypedDict, TypeVar, cast
 
 import torch
 from huggingface_hub import hf_hub_download
@@ -251,7 +251,7 @@ class ProcessorMigrationError(Exception):
 
 
 @dataclass
-class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
+class DataProcessorPipeline[TInput, TOutput](HubMixin):
     """A sequential pipeline for processing data, integrated with the Hugging Face Hub.
 
     This class chains together multiple `ProcessorStep` instances to form a complete
@@ -1432,8 +1432,8 @@ class DataProcessorPipeline(HubMixin, Generic[TInput, TOutput]):
 
 
 # Type aliases for semantic clarity.
-RobotProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
-PolicyProcessorPipeline: TypeAlias = DataProcessorPipeline[TInput, TOutput]
+RobotProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
+PolicyProcessorPipeline = DataProcessorPipeline[TInput, TOutput]
 
 
 class ObservationProcessorStep(ProcessorStep, ABC):

src/lerobot/robots/so_follower/config_so_follower.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 from dataclasses import dataclass, field
-from typing import TypeAlias
 
 from lerobot.cameras import CameraConfig
 
@@ -50,5 +49,5 @@ class SOFollowerRobotConfig(RobotConfig, SOFollowerConfig):
     pass
 
 
-SO100FollowerConfig: TypeAlias = SOFollowerRobotConfig
-SO101FollowerConfig: TypeAlias = SOFollowerRobotConfig
+SO100FollowerConfig = SOFollowerRobotConfig
+SO101FollowerConfig = SOFollowerRobotConfig

src/lerobot/robots/so_follower/so_follower.py

@@ -17,7 +17,6 @@
 import logging
 import time
 from functools import cached_property
-from typing import TypeAlias
 
 from lerobot.cameras.utils import make_cameras_from_configs
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
@@ -230,5 +229,5 @@ class SOFollower(Robot):
         logger.info(f"{self} disconnected.")
 
 
-SO100Follower: TypeAlias = SOFollower
-SO101Follower: TypeAlias = SOFollower
+SO100Follower = SOFollower
+SO101Follower = SOFollower

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -117,6 +117,13 @@ Modify tasks - set default task with overrides for specific episodes (WARNING: m
         --operation.new_task "Default task" \
         --operation.episode_tasks '{"5": "Special task for episode 5"}'
 
+Trim first 3 seconds from all episodes:
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht \
+        --new_repo_id lerobot/pusht_trim3s \
+        --operation.type trim_episode_start \
+        --operation.seconds 3.0
+
 Convert image dataset to video format and save locally:
     lerobot-edit-dataset \
         --repo_id lerobot/pusht_image \
@@ -170,6 +177,7 @@ from lerobot.datasets.dataset_tools import (
     modify_tasks,
     remove_feature,
     split_dataset,
+    trim_episode_start,
 )
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.utils.constants import HF_LEROBOT_HOME
@@ -215,6 +223,13 @@ class ModifyTasksConfig(OperationConfig):
     episode_tasks: dict[str, str] | None = None
 
 
+@OperationConfig.register_subclass("trim_episode_start")
+@dataclass
+class TrimEpisodeStartConfig(OperationConfig):
+    seconds: float | None = None
+    episode_indices: list[int] | None = None
+
+
 @OperationConfig.register_subclass("convert_image_to_video")
 @dataclass
 class ConvertImageToVideoConfig(OperationConfig):
@@ -464,6 +479,41 @@ def handle_modify_tasks(cfg: EditDatasetConfig) -> None:
         modified_dataset.push_to_hub()
 
 
+def handle_trim_episode_start(cfg: EditDatasetConfig) -> None:
+    if not isinstance(cfg.operation, TrimEpisodeStartConfig):
+        raise ValueError("Operation config must be TrimEpisodeStartConfig")
+
+    if cfg.operation.seconds is None:
+        raise ValueError("seconds must be specified for trim_episode_start operation")
+
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+    output_repo_id, output_dir = get_output_path(
+        cfg.repo_id, cfg.new_repo_id, Path(cfg.root) if cfg.root else None
+    )
+
+    if cfg.new_repo_id is None:
+        dataset.root = Path(str(dataset.root) + "_old")
+
+    logging.info(
+        f"Trimming first {cfg.operation.seconds}s from episodes "
+        f"{cfg.operation.episode_indices if cfg.operation.episode_indices else 'ALL'} in {cfg.repo_id}"
+    )
+    new_dataset = trim_episode_start(
+        dataset=dataset,
+        seconds=cfg.operation.seconds,
+        episode_indices=cfg.operation.episode_indices,
+        output_dir=output_dir,
+        repo_id=output_repo_id,
+    )
+
+    logging.info(f"Dataset saved to {output_dir}")
+    logging.info(f"Episodes: {new_dataset.meta.total_episodes}, Frames: {new_dataset.meta.total_frames}")
+
+    if cfg.push_to_hub:
+        logging.info(f"Pushing to hub as {output_repo_id}")
+        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()
+
+
 def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None:
     # Note: Parser may create any config type with the right fields, so we access fields directly
     # instead of checking isinstance()
@@ -594,6 +644,8 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
         handle_remove_feature(cfg)
     elif operation_type == "modify_tasks":
         handle_modify_tasks(cfg)
+    elif operation_type == "trim_episode_start":
+        handle_trim_episode_start(cfg)
     elif operation_type == "convert_image_to_video":
         handle_convert_image_to_video(cfg)
     elif operation_type == "info":

src/lerobot/teleoperators/so_leader/config_so_leader.py

@@ -15,7 +15,6 @@
 # limitations under the License.
 
 from dataclasses import dataclass
-from typing import TypeAlias
 
 from ..config import TeleoperatorConfig
 
@@ -38,5 +37,5 @@ class SOLeaderTeleopConfig(TeleoperatorConfig, SOLeaderConfig):
     pass
 
 
-SO100LeaderConfig: TypeAlias = SOLeaderTeleopConfig
-SO101LeaderConfig: TypeAlias = SOLeaderTeleopConfig
+SO100LeaderConfig = SOLeaderTeleopConfig
+SO101LeaderConfig = SOLeaderTeleopConfig

src/lerobot/teleoperators/so_leader/so_leader.py

@@ -16,7 +16,6 @@
 
 import logging
 import time
-from typing import TypeAlias
 
 from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.feetech import (
@@ -156,5 +155,5 @@ class SOLeader(Teleoperator):
         logger.info(f"{self} disconnected.")
 
 
-SO100Leader: TypeAlias = SOLeader
-SO101Leader: TypeAlias = SOLeader
+SO100Leader = SOLeader
+SO101Leader = SOLeader

src/lerobot/utils/io_utils.py

@@ -16,12 +16,10 @@
 import json
 import warnings
 from pathlib import Path
-from typing import TypeVar
 
 import imageio
 
 JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...]
-T = TypeVar("T", bound=JsonLike)
 
 
 def write_video(video_path, stacked_frames, fps):
@@ -33,7 +31,7 @@ def write_video(video_path, stacked_frames, fps):
         imageio.mimsave(video_path, stacked_frames, fps=fps)
 
 
-def deserialize_json_into_object(fpath: Path, obj: T) -> T:
+def deserialize_json_into_object[T: JsonLike](fpath: Path, obj: T) -> T:
     """
     Loads the JSON data from `fpath` and recursively fills `obj` with the
     corresponding values (strictly matching structure and types).

tests/datasets/test_dataset_tools.py

@@ -29,6 +29,7 @@ from lerobot.datasets.dataset_tools import (
     modify_tasks,
     remove_feature,
     split_dataset,
+    trim_episode_start,
 )
 from lerobot.scripts.lerobot_edit_dataset import convert_image_to_video_dataset
 
@@ -142,6 +143,104 @@ def test_delete_empty_list(sample_dataset, tmp_path):
         )
 
 
+def test_trim_episode_start_updates_indices(sample_dataset, tmp_path):
+    """Test trimming episode starts updates frame/timestamp/index metadata consistently."""
+    output_dir = tmp_path / "trimmed"
+    trim_seconds = 0.1  # 3 frames at 30 FPS
+    trim_frames = int(trim_seconds * sample_dataset.meta.fps)
+
+    with (
+        patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version,
+        patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download,
+    ):
+        mock_get_safe_version.return_value = "v3.0"
+        mock_snapshot_download.return_value = str(output_dir)
+
+        new_dataset = trim_episode_start(
+            sample_dataset,
+            seconds=trim_seconds,
+            output_dir=output_dir,
+        )
+
+    expected_length = 10 - trim_frames
+    assert new_dataset.meta.total_episodes == sample_dataset.meta.total_episodes
+    assert new_dataset.meta.total_frames == sample_dataset.meta.total_episodes * expected_length
+
+    indices = [int(i.item()) for i in new_dataset.hf_dataset["index"]]
+    assert indices == list(range(new_dataset.meta.total_frames))
+
+    episode_indices = [int(i.item()) for i in new_dataset.hf_dataset["episode_index"]]
+    frame_indices = [int(i.item()) for i in new_dataset.hf_dataset["frame_index"]]
+    timestamps = [float(i.item()) for i in new_dataset.hf_dataset["timestamp"]]
+
+    for ep_idx in range(sample_dataset.meta.total_episodes):
+        ep_frame_indices = [f for e, f in zip(episode_indices, frame_indices, strict=False) if e == ep_idx]
+        ep_timestamps = [t for e, t in zip(episode_indices, timestamps, strict=False) if e == ep_idx]
+
+        assert len(ep_frame_indices) == expected_length
+        assert ep_frame_indices == list(range(expected_length))
+        assert ep_timestamps[0] == pytest.approx(0.0)
+        assert ep_timestamps[-1] == pytest.approx((expected_length - 1) / sample_dataset.meta.fps)
+
+        ep_meta = new_dataset.meta.episodes[ep_idx]
+        assert int(ep_meta["length"]) == expected_length
+        assert int(ep_meta["dataset_from_index"]) == ep_idx * expected_length
+        assert int(ep_meta["dataset_to_index"]) == (ep_idx + 1) * expected_length
+
+
+def test_trim_episode_start_skips_too_short_episodes(tmp_path, empty_lerobot_dataset_factory):
+    """Test too-short episodes are skipped and remaining episodes are reindexed."""
+    features = {
+        "action": {"dtype": "float32", "shape": (2,), "names": None},
+        "observation.state": {"dtype": "float32", "shape": (2,), "names": None},
+        "observation.images.top": {"dtype": "image", "shape": (32, 32, 3), "names": None},
+    }
+    dataset = empty_lerobot_dataset_factory(root=tmp_path / "source", features=features)
+
+    for ep_len in [10, 2, 10]:
+        for _ in range(ep_len):
+            dataset.add_frame(
+                {
+                    "action": np.random.randn(2).astype(np.float32),
+                    "observation.state": np.random.randn(2).astype(np.float32),
+                    "observation.images.top": np.random.randint(0, 255, size=(32, 32, 3), dtype=np.uint8),
+                    "task": "task",
+                }
+            )
+        dataset.save_episode()
+    dataset.finalize()
+
+    trim_seconds = 0.1  # 3 frames at 30 FPS
+    with (
+        patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version,
+        patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download,
+    ):
+        mock_get_safe_version.return_value = "v3.0"
+        mock_snapshot_download.return_value = str(tmp_path / "trimmed")
+
+        new_dataset = trim_episode_start(
+            dataset,
+            seconds=trim_seconds,
+            output_dir=tmp_path / "trimmed",
+        )
+
+    # Episode 1 is too short and gets skipped. Remaining episodes are trimmed and reindexed.
+    assert new_dataset.meta.total_episodes == 2
+    assert new_dataset.meta.total_frames == 14
+    assert sorted({int(idx.item()) for idx in new_dataset.hf_dataset["episode_index"]}) == [0, 1]
+    assert [int(ep["length"]) for ep in new_dataset.meta.episodes] == [7, 7]
+
+
+def test_trim_episode_start_rejects_when_all_selected_are_too_short(sample_dataset, tmp_path):
+    """Test trimming fails when all selected episodes are too short and would be skipped."""
+    with pytest.raises(ValueError, match="All episodes selected for trimming are too short"):
+        trim_episode_start(
+            sample_dataset,
+            seconds=1.0,  # 30 frames > 10-frame episodes
+            output_dir=tmp_path / "trimmed",
+        )
+
+
 def test_split_by_episodes(sample_dataset, tmp_path):
     """Test splitting dataset by specific episode indices."""
     splits = {

tests/policies/test_policies.py

@@ -143,12 +143,18 @@ def test_policy(ds_repo_id, env_name, env_kwargs, policy_name, policy_kwargs):
     Note: We test various combinations of policy and dataset. The combinations are by no means exhaustive,
           and for now we add tests as we see fit.
     """
+    if policy_name == "vqbet" and DEVICE == "mps":
+        pytest.skip("VQBet does not support MPS backend")
+    if policy_name == "act" and "aloha" in ds_repo_id and DEVICE == "mps":
+        pytest.skip("ACT with aloha has batch mutation issues on MPS")
+
     train_cfg = TrainPipelineConfig(
         # TODO(rcadene, aliberts): remove dataset download
         dataset=DatasetConfig(repo_id=ds_repo_id, episodes=[0]),
         policy=make_policy_config(policy_name, push_to_hub=False, **policy_kwargs),
         env=make_env_config(env_name, **env_kwargs),
     )
+    train_cfg.policy.device = DEVICE
     train_cfg.validate()
 
     # Check that we can make the policy object.
@@ -227,6 +233,7 @@ def test_act_backbone_lr():
         dataset=DatasetConfig(repo_id="lerobot/aloha_sim_insertion_scripted", episodes=[0]),
         policy=make_policy_config("act", optimizer_lr=0.01, optimizer_lr_backbone=0.001, push_to_hub=False),
     )
+    cfg.policy.device = DEVICE
     cfg.validate()  # Needed for auto-setting some parameters
 
     assert cfg.policy.optimizer_lr == 0.01

tests/processor/test_pipeline.py

@@ -1870,9 +1870,7 @@ class NonCallableStep(ProcessorStep):
 
 def test_construction_rejects_step_without_call():
     """Test that DataProcessorPipeline rejects steps that don't inherit from ProcessorStep."""
-    with pytest.raises(
-        TypeError, match=r"Can't instantiate abstract class NonCallableStep with abstract method __call_"
-    ):
+    with pytest.raises(TypeError, match=r"Can't instantiate abstract class NonCallableStep"):
         DataProcessorPipeline([NonCallableStep()])
 
     with pytest.raises(TypeError, match=r"must inherit from ProcessorStep"):

tests/scripts/test_edit_dataset_parsing.py

@@ -28,6 +28,7 @@ from lerobot.scripts.lerobot_edit_dataset import (
     RemoveFeatureConfig,
     SplitConfig,
     _validate_config,
+    TrimEpisodeStartConfig,
 )
 
 
@@ -47,6 +48,7 @@ class TestOperationTypeParsing:
             ("merge", MergeConfig),
             ("remove_feature", RemoveFeatureConfig),
             ("modify_tasks", ModifyTasksConfig),
+            ("trim_episode_start", TrimEpisodeStartConfig),
             ("convert_image_to_video", ConvertImageToVideoConfig),
             ("info", InfoConfig),
         ],
@@ -77,6 +79,7 @@ class TestOperationTypeParsing:
             ("merge", MergeConfig),
             ("remove_feature", RemoveFeatureConfig),
             ("modify_tasks", ModifyTasksConfig),
+            ("trim_episode_start", TrimEpisodeStartConfig),
             ("convert_image_to_video", ConvertImageToVideoConfig),
             ("info", InfoConfig),
         ],

tests/utils.py

@@ -22,8 +22,9 @@ import torch
 
 from lerobot import available_cameras, available_motors, available_robots
 from lerobot.utils.import_utils import is_package_available
+from lerobot.utils.utils import auto_select_torch_device
 
-DEVICE = os.environ.get("LEROBOT_TEST_DEVICE", "cuda") if torch.cuda.is_available() else "cpu"
+DEVICE = os.environ.get("LEROBOT_TEST_DEVICE", str(auto_select_torch_device()))
 
 TEST_ROBOT_TYPES = []
 for robot_type in available_robots: