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 @jiaman01's thread below 👇 🌐 Project: vision-language-kinematics.g… 📄 Paper: arxiv.org/abs/2606.30645 🎦 Video: youtu.be/ZB6k_iMJP7M Huge thanks to my amazing collaborators @jiaman01 @eric_srchen @TakaraTruong @ Pei Xu, and to our advisors @pabbeel @rocky_duan @KoushilSreenath @akanazawa @carlo_sferrazza @GuanyaShi @ckarenliu.