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@LeRobotHF • 16,312 subscribers

~ Lowering the barrier to entry for robotics ~ Crafted with care by @HuggingFace 🤗 Join our discord: https://t.co/Sx2jdT0jeF

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We built a bipedal robot for about $2,500. A real, mostly 3D-printed robot you can build, repair, simulate, train, and control. Today we’re releasing LeRobot Humanoid: an open robot-learning platform with hardware, runtime, identification tools, and training environments. Blog post: Repo:

We built a bipedal robot for about $2,500. A real, mostly 3D-printed robot you can build, repair, simulate, train, and control. Today we’re releasing LeRobot Humanoid: an open robot-learning platform with hardware, runtime, identification tools, and training environments. Blog post: Repo:

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MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

MolmoAct2 is landing in LeRobot! Ai2's open Action Reasoning Model combines a Molmo2-ER vision-language backbone with a flow-matching continuous action expert to predict robot action chunks from images, language instructions, and proprioceptive state. An open robot foundation model built for real-world control, with strong out-of-the-box performance and easy fine-tuning in LeRobot. Pick-and-place inference running on NVIDIA DGX Spark! Blog: Paper: Thanks to Ai2 Jiafei Duan Haoquan Fang

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🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

🤖 NVIDIA’s Gr00t N1.5 is now available in LeRobot! This is the result of a great collaboration between the Hugging Face LeRobot team and NVIDIA Robotics ! Gr00t N1.5 highlights: 🦾 Cross-embodiment foundation model for robots 🧠 Multimodal inputs: vision, language, and proprioception 🪛Tested on the Libero benchmark and real-world hardware tasks 🌍Trained on real robot, synthetic, and internet-scale video data ⚙️ Flow matching action transformer for action prediction

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With two SO100 leaders, and followers, you can now create a bimanual setup out of the box in LeRobot! 🦾 And we’re just getting started, more robots are on the way! 🤖 Which one would you like to see next? ⏩

With two SO100 leaders, and followers, you can now create a bimanual setup out of the box in LeRobot! 🦾 And we’re just getting started, more robots are on the way! 🤖 Which one would you like to see next? ⏩

17,805 просмотров

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Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog →

Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog →

279,349 просмотров • 1 месяц назад

🤖Adding new RL algorithms to LeRobot just got much easier. Demo: HIL-SERL training with a SAC-based RL algorithm on an SO-100 for a hole-in-hand peg-in-hole task. Sparse reward, only 30 offline demos mixed with live robot experience, and ~1 hour of online training with human interventions only when the policy fails. The bottom graph tracks intervention rate: high at the start, steadily dropping as the policy improves. The refactor separates algorithm logic from training infrastructure: • RLAlgorithm owns learning logic • RLTrainer handles orchestration • DataMixer combines rollouts, demos, interventions, and future data sources Adding an RL algorithm now looks much closer to adding a policy: one algorithm file, one config, one registry entry. SAC is first. RLT, RECAP, ConRFT, QC-FQL, DSRL, and VLA RL fine-tuning next! Thomas Wolf clem 🤗

🤖Adding new RL algorithms to LeRobot just got much easier. Demo: HIL-SERL training with a SAC-based RL algorithm on an SO-100 for a hole-in-hand peg-in-hole task. Sparse reward, only 30 offline demos mixed with live robot experience, and ~1 hour of online training with human interventions only when the policy fails. The bottom graph tracks intervention rate: high at the start, steadily dropping as the policy improves. The refactor separates algorithm logic from training infrastructure: • RLAlgorithm owns learning logic • RLTrainer handles orchestration • DataMixer combines rollouts, demos, interventions, and future data sources Adding an RL algorithm now looks much closer to adding a policy: one algorithm file, one config, one registry entry. SAC is first. RLT, RECAP, ConRFT, QC-FQL, DSRL, and VLA RL fine-tuning next! Thomas Wolf clem 🤗

27,898 просмотров • 22 дней назад

This is the full video of the hardest version of the task: t-shirt folding from unstructured initial states. This setting really requires at least some strategy, since the robot first has to spread the shirt before it can complete the fold. Full details on data collection strategies in the blog below. 👇

This is the full video of the hardest version of the task: t-shirt folding from unstructured initial states. This setting really requires at least some strategy, since the robot first has to spread the shirt before it can complete the fold. Full details on data collection strategies in the blog below. 👇

53,212 просмотров • 1 месяц назад

Our folding project showed what is possible with open-source. Reproducing it shouldn't take a PhD. LeRobot now ships with AGENT_GUIDE.md → so now your AI agent knows the insights, how to set up an SO-101, record good data, and pick a policy for your GPU. Just ask!

Our folding project showed what is possible with open-source. Reproducing it shouldn't take a PhD. LeRobot now ships with AGENT_GUIDE.md → so now your AI agent knows the insights, how to set up an SO-101, record good data, and pick a policy for your GPU. Just ask!

30,553 просмотров • 1 месяц назад

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

🚀 We just shipped a big upgrade to our imitation-learning-in-simulation playground in LeRobot, built together with Lightwheel ! You can now teleoperate robots in sim (keyboard or real robot) and collect training demos instantly. This makes it possible to run real IL research on harder, more realistic manipulation tasks, even if you don’t have hardware. New tasks: 🟠 pick orange to the plate 🧺 fold cloth (yes!) 📦 pick 2 “e” toys to the box 🔴 lift red cube Through our partnership with Lightwheel, LeIsaac was integrated into EnvHub on day one, a best-practice integration that strengthens both ecosystems and pushes simulation-first robotics forward. 👉 Load a task via EnvHub, start teleop-ing, record your demo, upload the data on the hub and you’re ready to train or test.

44,492 просмотров • 6 месяцев назад

🚀 Big news: we just added Reachy 2 to LeRobot! Huge thanks to our friends at @PollenRobotics 💛🤗 Reachy 2 is also available in simulation, so you can try it out right away. 🎥 Check out the teleop & autonomous demo below!

🚀 Big news: we just added Reachy 2 to LeRobot! Huge thanks to our friends at @PollenRobotics 💛🤗 Reachy 2 is also available in simulation, so you can try it out right away. 🎥 Check out the teleop & autonomous demo below!

34,228 просмотров • 9 месяцев назад

We’ve teamed up with X Square Robot to integrate WALL-OSS, a powerful new VLA foundation model into LeRobot!

We’ve teamed up with X Square Robot to integrate WALL-OSS, a powerful new VLA foundation model into LeRobot!

21,294 просмотров • 5 месяцев назад

Introducing Subtask Annotations in LeRobot Datasets. LeRobot now supports subtasks as a native dataset field, so you can label task progression frame-by-frame, train hierarchical policies, and build stronger reward models with stage-aware supervision. Available today, with an annotation space + tooling to easily add subtasks and high-level dialogue to your dataset.

Introducing Subtask Annotations in LeRobot Datasets. LeRobot now supports subtasks as a native dataset field, so you can label task progression frame-by-frame, train hierarchical policies, and build stronger reward models with stage-aware supervision. Available today, with an annotation space + tooling to easily add subtasks and high-level dialogue to your dataset.

16,438 просмотров • 4 месяцев назад

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