LeRobot format format for robot training data

Quick facts

Origin

Hugging Face — LeRobot framework released 2024, Apache-2.0 license, github.com/huggingface/lerobot.

Datasets

181 datasets in the public LeRobot collection on Hugging Face Hub.

Supported policies

10 architectures: ACT, Diffusion, VQ-BeT, HIL-SERL, TDMPC, π0, π0.5, GR00T N1.5, SmolVLA, XVLA.

Format

Synchronized MP4 videos + Parquet tables for state/action streams; LeRobotDataset v2.0 / v2.1.

Simulators

LIBERO and MetaWorld benchmarks supported.

Comparison

What is LeRobot format?

LeRobot format should be requested when the buyer's training or evaluation pipeline already expects developer-friendly robot learning datasets and policy training pipelines. Anchor the bounty to the canonical specification before suppliers submit samples ^[1], then use implementation documentation to make the expected file layout reviewable ^[2]. Robotics teams should also name the dataset or paper lineage they expect suppliers to support ^[3].

"LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch."

For truelabel buyers, that quote matters because it turns LeRobot format from a generic delivery preference into a source-backed requirement the supplier can test against a sample file.

Using LeRobot format with robot data

A useful LeRobot format sample should prove episode metadata, observation tensors, action tensors, timestamps, and repo-compatible manifest, plus file naming, manifest completeness, timestamp behavior, and rejected-example traceability. Include at least one workflow or converter reference so the supplier can show how the files load in practice ^[4], one interoperability reference for adjacent formats ^[5], and one comparison source for why this format is preferable to a raw folder dump ^[6].

Related pages

Use these to move from category-level context into specific task, dataset, format, and comparison detail.

External references and source context

1. LeRobot repository

   LeRobot provides robotics models, datasets, and tools for PyTorch workflows.

   GitHub ↩
2. LeRobot documentation

   Hugging Face publishes LeRobot documentation for robotics dataset workflows.

   Hugging Face ↩
3. LeRobot dataset documentation

   LeRobot dataset documentation defines dataset packaging expectations.

   Hugging Face ↩
4. LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch

   The LeRobot paper frames the library as real-world robotics tooling.

   arXiv ↩
5. RLDS: Reinforcement Learning Datasets

   RLDS is a related robotics episode format for conversion planning.

   GitHub ↩
6. HDF5 1.14 documentation

   HDF5 is relevant to LeRobot-compatible robot episode storage.

   The HDF Group ↩

FAQ