AIエージェント
SWE-Gym
SWE-Gym: the first training environment for real-world software engineering agents and verifiers (ICML 2025).
★ 748 スターApache-2.0
ガイドを見る →lerobotは、huggingfaceが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: huggingface/lerobot）。GitHubスターは28,013、フォークは5,837で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月8日です。
Hugging Face's LeRobot: datasets, models and tools to make real-world robotics AI accessible
Decide what you want to achieve with lerobot and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
A standard format for robot data, loadable from the Hub in a few lines with video handling built in.
Implementations of leading imitation/reinforcement policies ready to train and evaluate.
APIs to connect to supported robots, read observations and send actions.
Evaluate policies on standard benchmarks — the README shows evaluation on LIBERO.
Extension packages for additional robots and teleoperators, including third-party hardware.
Share datasets and trained policies through the Hugging Face Hub like models and datasets elsewhere.
Get the LeRobot source and examples.
git clone https://github.com/huggingface/lerobot.git
cd lerobotInstall LeRobot from PyPI (add robot/teleoperator packages for your hardware as documented).
pip install lerobotPull a dataset from the Hub using the README's example, then follow the docs to train or evaluate a policy.
# from lerobot.datasets.lerobot_dataset import LeRobotDataset
# dataset = LeRobotDataset('lerobot/pusht') # see README 'LeRobot Dataset'lerobot is maintained by the huggingface organization/account on GitHub, together with open-source contributors, in the public repository huggingface/lerobot.
Yes — the code is open source under the Apache-2.0 license. You can use, modify and redistribute it under that license's terms.
No API key — LeRobot is free and Apache-2.0. Costs are hardware: a supported robot arm and a machine with a GPU for training.
ROS excels at robot middleware; LeRobot focuses on the learning stack — datasets, policies and evaluation — in one Hub-integrated Python library.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
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