Dandan Shan

Want a better model to reconstruct 3D hands🖐️ from RGB images? Don't miss our latest work and fresh release, HaMeR, on Hand Mesh Recovery!!! Code and models are released. More video demos are on the project page!

@yingwww_ @Meta Thank you Ying!

@DandanShan_ @Meta Congrats Dandan!!🥳🥳🥳🥳

It's been super fun working with the team on exciting problems. The journey ahead is even more thrilling @Meta 🤖🚀 Let's go!

@kevin_y_wu Thanks, Kevin!

Excited to see what you do @DandanShan_ !

Excited to share that ARI (Assured Robot Intelligence) is joining @Meta! When we co-founded ARI a year ago, the mission was clear: build humanoid intelligence for the real world. Joining Meta Superintelligence Labs (MSL), we'll continue advancing frontier robotics models toward physical superintelligence in the physical world. Huge thanks to my co-founders, the incredible ARI team, and our investors led by @aixventureshq for backing this from day one. This is just the beginning.

ARI is joining @Meta! Over the past year, we have been building ARI (Assured Robot Intelligence) with the mission to build industry-grade physical AI for humanoids. The ARI stack is built on human experience, condensed into actionable tokens that can be rapidly adapted to real-world hardware. But the most rewarding part of ARI has been the people. I feel truly blessed to have worked alongside some of the world's best roboticists, a top-notch investor pool led by @aixventureshq, and the many supporters pushing for us behind the scenes. Starting next week, ARI will join the Meta Superintelligence Labs (MSL) to continue advancing frontier robotics models that advance personal superintelligence in the physical world. We have the potential to transform AI that can think and talk to AI that can do, assisting humans safely and reliably in the physical world. To the many people behind the scenes who supported us: Thank you! This is just the beginning. More in the Bloomberg article:

How can human data help train robots? Can we control digital avatars like robots? How to make robots behave more like humans? Our CVPR 2026 workshop Agents in Interactions: From Humans to Robots is back this summer. Contribute to this exciting direction, submit your work by May 8

We're thrilled to organize the 2nd Workshop on Agents in Interactions: From Humans to Robots! Submit your best work by May 8 and join us at CVPR in Denver to discuss research in this exciting space w/ @yufei_ye @DandanShan_ @jiaman01 @xiaolonw Alan Yuille

🚨 Call for Papers! Deadline: May 8! The 2nd Workshop on Agents in Interactions: From Humans to Robots at #CVPR2026. We invite submissions on understanding and learning interactions involving humans and robots. w/ @yufei_ye @mangahomanga @jiaman01 @xiaolonw Alan Yuille

Learning from human data requires human-like hardware. Humans use their wrists constantly, but table-top manipulators lack this flexibility. We build upon RUKA and introduce RUKA-v2: a tendon-driven hand with a 2-DOF wrist and finger abduction/adduction 👋✌️

@dimadamen @ahmad_darkhalil @bristolcs @CSProfKGD @chriswolfvision Congratulations!! @ahmad_darkhalil

@ahmad_darkhalil @bristolcs @CSProfKGD @chriswolfvision Full details of Ahmad's work are on his webpage: ahmaddarkhalil.github.io Ahmad is staying a little longer as a postdoc at our group, to continue his excellent line of work.

Congrats @ahmad_darkhalil who passed his PhD today @bristolcs, examined by @CSProfKGD & @chriswolfvision. Thesis includes his excellent work on: VISOR, EPIC Fields, HD-EPIC [accessible from: epic-kitchens.github.io], EgoPoints [ahmaddarkhalil.github.io//EgoPoints/], &Hand-Object Detection.

We don't need the name of an object to pick it up; we simply need to know where it is and what it looks like. Introducing Contact-Anchored Policies (CAPs): instead of language, we explicitly condition on contacts. Our policy learns object pickup with only 16 hours of data! 🧵

Best ideas are often the simplest in hindsight. Meet Contact-Anchored Policies (CAP)🧢: by conditioning policies on physical contact (vs language) we achieve env & embodiment generalization with super low resources. This policy ⬇️ learned to pick from scratch w/ 16 hrs of data 🧵

Introducing FoundationMotion. A large-scale, video-derived motion annotation dataset & auto-labeling pipeline + advanced models for motion understanding. Fully open-source: code, datasets, and models, free to use and build on. Understanding motion is core to physical reasoning, yet today’s leading models still struggle with simple spatial actions like “turn right” or “move up” or “flip the toast” - mainly due to the lack of large, fine-grained motion datasets. We present FoundationMotion, a fully automated pipeline that: • detects & tracks objects in videos • extracts trajectories • uses LLMs + frames to generate rich motion captions & QA pairs → creating large-scale, high-quality motion datasets at scale. After fine-tuning the open-source models Qwen and NVILA on our annotations, these models now outperform the closed-source Gemini-3-Flash and GPT-5.1 on spatial understanding tasks across autonomous driving, robotics, and everyday scenarios. 📜Paper: arxiv.org/abs/2512.10927 🌐Webpage: yulugan.com/projects/Found… 💻 Code: github.com/Wolfv0/Foundat… 🕸️Model: huggingface.co/WoWolf/models 📊 Dataset: huggingface.co/datasets/WoWol… 👉 Interactive Demo: huggingface.co/spaces/yulu2/F… Let’s move research forward together. FoundationMotion is also referred to as Wolf V2 🐺, the second chapter in the Wolf series: wolfv0.github.io.

Dexterous manipulation by directly observing humans - a dream in AI for decades - is hard due to visual and embodiment gaps. With simple yet powerful hardware - Aria 2 glasses 👓 - and our new work AINA 🪞, we are now one significant step closer to achieving this dream.

When @anyazorin and @irmakkguzey open-sourced the RUKA Hand (a low-cost robotic hand) earlier this year, people kept asking us how to get one. Open hardware isn’t as easy to share as code. So we’re releasing an off-the-shelf RUKA, in collaboration with @WowRobo and @zhazhali01.

I'll be joining the faculty @JohnsHopkins late next year as a tenure-track assistant professor in @JHUCompSci Looking for PhD students to join me tackling fun problems in robot manipulation, learning from human data, understanding+predicting physical interactions, and beyond!

✨ We were honored to deliver some of the very first NVIDIA DGX Sparks to AI Pioneer @ylecun and AI Researcher, @soumithchintala, from @Meta and @NYUniversity in NYC. “Every PhD student in AI should have one of these,” said Yann. We couldn’t agree more. We are anticipating great things from their visionary AI research. Learn more: nvda.ws/42EGKOM #SparkSomethingBig 💫

I gave a Early Career talk at CoRL 2025 in Seoul last week, where I talked about my observations from the past decade in robot learning along with where the field is headed for the next decade. In summary, the future of robot learning needs: (1) Data beyond teleop: We are never going to reach the scale of LLM / VLM data by tele-operating robots. Need to leverage consumer hardware already in people's hands (e.g. iPhones) and emerging devices (e.g. Smartglasses). (2) Observations beyond vision: The hard problem in robotics is dexterity. Dexterity is all about moving objects intricately through contact. The sense of touch is critical for this. Vision can help you acquire objects, but anything more complex will need touch. (3) Reasoning beyond reactivity: The biggest wins in robot learning have been in reactive policies (both manipulation and locomotion). But the class of models that got us here are generally feed-forward nets. Long-horizon reasoning needs the ability to predict future outcomes and manipulate them. Currently unclear what the right scalable architectures are here, but we are working on it. (thanks @zacinaction for the pic!)

@notmahi @LerrelPinto @JitendraMalikCV @berkeley_ai @AIatMeta Congratulations! 🎉

Happy to share that I'm a Dr. now! 🎓🎉 I'm indebted to @LerrelPinto for his 🔥 advice & mentorship, and an unforgettable five years. Next up, I'm starting a postdoc with @JitendraMalikCV, spending time at @berkeley_ai & @AIatMeta. Would love to say hi if you're in the bay area!

Hello! If you are interested in dynamic 3D or 4D, don't miss the oral session 3A at 9 am on Saturday: @zhengqi_li will be presenting "MegaSaM" I'll be presenting "Stereo4D" and @QianqianWang5 will be presenting "CUT3R"