Lujie Yang

Our first podcast episode is finally live! I personally loved the casual chat after the presentation (From 46:05 onwards). Sorry about the audio—my mic didn't record, so I had to re-record my lines as best as I could remember them. And huge thanks to @LujieYang0 and @chris_j_paxton. Looking forward to seeing more research published on our Booster robots 😎

Can humanoids perform agile, autonomous, long-horizon parkour—based on what they see in the world? We present 𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝘃𝗲 𝗛𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗣𝗮𝗿𝗸𝗼𝘂𝗿 (𝗣𝗛𝗣): a framework that chains dynamic human skills using onboard depth perception for long-horizon traversal. 1/6

🚀 Excited to announce the Workshop on Manipulation Robustness at #ICRA2026 in Vienna 🇦🇹 (June 5)! 🌐 manipulation-robustness.github.io/icra2026/ Despite rapid progress, achieving robust, contact-rich manipulation in real-world uncertainty remains a core challenge. This workshop connects fundamental principles with practical pathways toward robust manipulation systems. 🎤 Invited speakers: @andrea_bajcsy, @HaozhiQ, @Jan_R_Peters, @SongShuran, Jie Song, Josie Hughes, Oliver Brock 📝 Call for Papers: Short papers / extended abstracts (2–4 pages) 📅 Deadline: May 5, 2026 🔗 Submission portal: OpenReview (openreview.net/group?id=IEEE.…) 🏆 Best Paper Awards (two robotic arms🤖 and cash prize) — sponsored by @ARXrobotics! Organized by @YifeiDong314159, @Kingchou007Li, @Hui_Zhang_eth, Teng Xue, @s_zhanyi, Quan Gao, Manuel Baum, @lujieyang98, Sylvain Calinon, @ftpokorny, @ChengXianyi. #Robotics #Manipulation #ICRA

I've long wondered if we can make a humanoid robot do a 𝘄𝗮𝗹𝗹𝗳𝗹𝗶𝗽 - and we just made it happen by leveraging 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁 with BeyondMimic tracking! This came after our original OmniRetarget experiments, with only minor tweaks to RL training: relaxing a termination threshold and removing one reward term. The policy achieved a 𝟱/𝟱 success rate in our real-world experiments, showing the strength of high-quality, interaction-preserving motion retargeting combined with BeyondMimic’s minimal RL tracking. Here is the updated arXiv: arxiv.org/abs/2509.26633 (In Sec. V. A)

Humanoid motion tracking performance is greatly determined by retargeting quality! Introducing 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁🎯, generating high-quality interaction-preserving data from human motions for learning complex humanoid skills with 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 RL: - 5 rewards, - 4 DR terms, - Proprio. ONLY, - NO history/curriculum. Ready for agile, human-like 🤖? (Best with 🎧) 🔗 omniretarget.github.io 🎥 1/9

Excited to organize Workshop on Learning Meets Model-Based Methods for Contact-Rich Manipulation @ ICRA 2025! We welcome submissions on a range of topics—check out our website for details:
 contact-rich.github.io Join us for an incredible lineup of speakers! #ICRA2025

While neural networks have made a seismic impact on the design of robotic controllers, it’s been difficult to verify the safety & stability of complex robots powered by them 🧵 Researchers from MIT CSAIL & elsewhere have developed approaches to rigorously certify Lyapunov conditions in complicated robotic systems. Their algorithm creates neural network controllers with stability guarantees for robots, potentially enabling safer deployment for drones & AVs: bit.ly/3Yd3KDf

The next honorable mention for the 2023 IEEE Transactions on #Robotics King-Sun Fu Memorial Best Paper Award is a paper by researchers @MIT_CSAIL that enables global planning for contact rich system using local smoothing. ieeexplore.ieee.org/document/10225… #SmoothingMethods

Introducing a “nonlinear equivalent” of LQR controllers! ⚙️ CSAIL researchers synthesize approximate optimal controllers for nonlinear dynamical systems w/optimality & stability guarantees. To achieve this, their work finds both under-and over-approximations of the value function via sums-of-squares. A thread 🧵