Yishu Li

A closed door looks the same whether it pushes or pulls. Two identical-looking boxes might have different center of mass. How should robots act when a single visual observation isn't enough? Introducing HAVE 🤖, our method that reasons about past interactions online! #CORL2025

Meet KinDER — a stress test for robot physical reasoning. All 13 methods failed 😈 🌎 25 environments ♾️ Infinite tasks 🏋️ Gymnasium API ⚒️ Over 20 parameterized skills 🪧 Human demonstrations 📊 13 baselines (planning and learning) From @Princeton @CMU_Robotics @ICatGT @CambridgeMLG @nvidia @MIT_CSAIL 🧵 1/n

@hq_fang @Stanford @StanfordSVL @drfeifei congrats Haoquan!

I’m excited to share that I’ve decided to join @Stanford @StanfordSVL as a CS PhD student, advised by @drfeifei! I feel very fortunate for all the opportunities I’ve had so far, and I’m genuinely thrilled for this next chapter. I’m eager to dive deeper into robot learning in such an inspiring environment, and to continue developing as a researcher alongside people I deeply admire. I want to sincerely thank @RanjayKrishna, Ali Farhadi, @JenqH, @DJiafei, and everyone who has guided, encouraged, and believed in me along the way. I’m also especially grateful to @uwcse and @allen_ai for providing such a wonderful community and so many meaningful opportunities. I also truly appreciate the time and support from @drfeifei, @jiajunwu_cs, @RuohanZhang76, @ManlingLi_, @wenlong_huang, @YunfanJiang, @wensi_ai, and many others throughout both my application and decision process. I’m really looking forward to learning from and working with you all at Stanford! Stay tuned for more exciting updates!

Great work, Yuanchen!

Thrilled to share one of my favorite works this year: DexNDM! We bridge the Sim2Real gap for dexterous in-hand rotation, achieving a true "0-to-1" advancement. The key? DexNDM learns from biased, real-world data without needing any successful demonstrations. Now a general-purpose dexterous hand can stably rotate large books, long rods, & complex objects around any axis, from any wrist pose. This powerful primitive enables complex, long-horizon tasks like teleoperated screwing and furniture assembly. 📄 Paper: arxiv.org/abs/2510.08556 🌐 Project Page: meowuu7.github.io/DexNDM/

Introducing FMVP: a method that adapts to natural arm motions during robot-assisted dressing. Pre-trained on vision in sim, fine-tuned with limited real-world vision+force data, and tested in a 12-user, 264-trial study, FMVP is robust across garments and motions. #CoRL2025

How do we discover a robot's failure modes before deploying it in the real world? Standard benchmarks often don't capture the full picture, leaving policies vulnerable to plausible variations in object shape. Thrilled that our work, "Geometric Red-Teaming for Robotic Manipulation," has been accepted as an oral presentation at #CoRL2025! We introduce a framework to automatically find these geometric blindspots. georedteam.github.io 🧵

Check out @_ksaha's very cool work on learning and planning for long-horizon tasks!

@_ksaha Thanks!!!

Check out @LisaYishu's cool work on reasoning about past interactions!

@AveryJuuu0213 Thanks congrats to you toooooooo

Great Work! Congrats Yishu!!🔥

Come check out our poster at Poster Session 1 on Sept 28 @ 4:30 - 6:00PM at #CoRL2025! Website: liy1shu.github.io/HAVE_CoRL25/ Paper: arxiv.org/abs/2509.00271 Code: github.com/liy1shu/HAVE_C… Thanks to collaborators: @shhmxy2, @Ying_yyyyyyyy, @ktsim01, @BenAEisner, and @davheld.

We analyzed how the number of action proposals provided to the verifier affects performance. The method is sample efficient, as performance improves significantly when the verifier is given just 5 samples. This demonstrates the efficiency of using a verifier for action selection.

A closed door looks the same whether it pushes or pulls. Two identical-looking boxes might have different center of mass. How should robots act when a single visual observation isn't enough? Introducing HAVE 🤖, our method that reasons about past interactions online! #CORL2025

🔍 How can we build AI agents that reason about the physical world the way humans do (or better) ? Excited to share Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel, which will be presented next Thursday July 17th at ICML in Vancouver! 👇(1/6)

Thrilled to share our new paper SAFE🤖! A robust failure detector for Vision-Language-Action models! This was my first time working on real-world robot experiments, and many moments over the past few months have made me excited for my robotics journey😻😻

Introducing ArticuBot🤖at #RSS2025, in which we learn a single policy for manipulating diverse articulated objects across 3 robot embodiments in different labs, kitchens & lounges, achieved via large-scale simulation and hierarchical imitation learning. articubot.github.io 🧵