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How can we scale perception-based humanoid learning without collecting massive humanoid teleoperation data? 🚀 Excited to finally share VLK! What excites me most about VLK is that it reframes data collection as a data generation problem. Instead of relying on expensive humanoid teleoperation, we automatically generate synchronized vision, language,... and whole-body kinematics from reconstructed real-world scenes. Making this vision a reality required bridging three fundamental challenges: 👀 Perception: Bridging the RGB sim → real gap through visual domain randomization and motion blur mitigation during both training and deployment. 🤖 Embodiment: Bridging the kinematics → dynamics gap with real-time VLA deployment, test-time RTC, and SceneBot, enabling seamless deployment on a real humanoid. 🌍 Environment: Bridging the real-world → synthetic gap to enable scalable Vision-Language-Kinematics data generation through scene reconstruction and interaction synthesis. It has been an amazing journey working with such an incredible team. For a complete walkthrough of the project, check out Jiaman Li's thread below 👇 🌐 Project: 📄 Paper: 🎦 Video: Huge thanks to my amazing collaborators Jiaman Li Sirui Chen Takara Truong @ Pei Xu, and to our advisors Pieter Abbeel Rocky Duan Koushil Sreenath Angjoo Kanazawa Carlo Sferrazza Guanya Shi Karen Liu.show more

Yen-Jen Wang ✈️ CoRL

1,158 subscribers

879,809 Aufrufe • vor 4 Monaten •via X (Twitter)

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11 Kommentare

Profilbild von Yen-Jen Wang

Yen-Jen Wangvor 4 Monaten

In addition, SceneBot serves as the whole-body tracker behind VLK, bridging the kinematics → dynamics gap and making robust real-world humanoid deployment possible. If you're interested in the tracking side, definitely check out the SceneBot thread 👇

Profilbild von Felix

Felixvor 4 Monaten

This are just pure children games

Profilbild von EnzeLI

EnzeLIvor 4 Monaten

Awesome ! 👏

Profilbild von Kashif Ali Khan

Kashif Ali Khanvor 4 Monaten

synthetic data for humanoids is actually such a cool direction ngl

Profilbild von Maryam

Maryamvor 4 Monaten

Amazing work!

Profilbild von Zeo

Zeovor 4 Monaten

tbh you don't need teleop scale, you need better sim-to-real with synthetic variation.

Profilbild von Marilyn Liu

Marilyn Liuvor 3 Monaten

podcast and article are on the way!

Profilbild von Jerry Chéng ✈️ IROS 2026

Jerry Chéng ✈️ IROS 2026vor 4 Monaten

Amazing work Yen Jen! Love to visit ur lab some day;)

Profilbild von clankr

clankrvor 4 Monaten

One of the most enjoyable papers to read this week. Most surprising part is its performance under highly challenging lighting, where many vision-based models/pipelines just fail. The party lights were the ultimate flex.

Profilbild von Yen-Jen Wang

Yen-Jen Wangvor 4 Monaten

Thank you for your interest in our paper!❤️

Profilbild von غدير 🇸🇦🇸🇦🇸🇦

غدير 🇸🇦🇸🇦🇸🇦vor 4 Monaten

تم

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Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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