Thrilled to share that I'll be joining the University of Michigan as an Assistant Professor in Fall 2027! My lab will work on Robot Learning, Dexterous Manipulation, Robot Foundation Models & 3D Perception. I'm looking for students to join me. Please apply and reach out. (1/2)

Thanks everyone for your warm wishes 🙏Will share more updates soon!

I'll be joining @UMRobotics with an affiliation in CS (@UMichCSE). None of this would have been possible without my advisor @jiadeng , my mentors Dieter Fox and @abhishekunique7, and the constant love and support of my family. Thank you all! I can't wait to get started. More on what's next soon! (2/2)

This startup, Proception, is taking a unique approach to collecting training data to tackle one of the hardest problems in robotics: hands. spr.ly/6019BDvqR5

ProHand & ProGlove are shipping The most human-like, capable robot hand ever built. Hardware data, end-to-end. We’ve raised $11M to accelerate this.

OG-VLA has been accepted to @iros2026! 🎉 It is one of the earliest works exploring the use of 3D priors within VLAs. Such approaches provide both computational and data efficiency. Check it out 👇

This is a nice read. It shows a path for the knowledge economy in the AI era.

Humanoids will have superhuman reaction time. ProHand by Proception

An important reminder! Sometimes simple modular frameworks are enough. Great work by @WillShenSaysHi & @nishanthkumar23

Evaluation is a critical bottleneck in building robot foundation models. Check out our latest work RoboLab, led by @xuningy, which addresses this exact challenge. Its a high-fidelity simulation environment for testing these models. A truly generalist policy should be able to complete these tasks zero-shot, and this benchmark highlights exactly how far we still have to go. More info 👇

When every generalist robot model scores 95% on a benchmark, the numbers become meaningless. What if we built a photorealistic benchmark that never saturates and can generate new scenes and tasks with AI Workflows in minutes? We introduce RoboLab! 🧵(1/6)

They also introduce Textual FAST, outperforms plain FAST, textual time-based actions and discrete tokens. The bigger takeaway is that robustness can come from good action representation and inference design. Not just only scale. (3/3)

A diffusion expert drafts candidate actions, then the textual action VLM verifies in one pass. Textual actions improve both performance and policy behaviour: more recovery attempts, fewer pre-grasp collisions, better under shift. arxiv.org/abs/2603.18091 (2/3)

Shoutout to Zhao et al. for ADV. A great follow-up to VLA-0! The idea is simple and the numbers are impressive. On LIBERO, ADV gets about the same perf as Cosmos Policy, ~10x cheaper to train by my back-of-the-envelope math, and runs at 40Hz. Nice read if you work on VLAs. (1/3)

Happy to share that the code for VLA-0 is out now: github.com/NVlabs/vla0 Given its simplicity, it’s a great starting point to try out VLAs!

To my friends and family in India Please raise your voice and DEMAND clean air! It is your fundamental right. Think about the youngest member of your family. What have they done to lose years of their life just because they are born in India. Enough of ignorance.

The launch of the first humanoid for consumers, Neo-X, is truly exciting! Many are claiming this means robot learning is solved and that 1X has leapfrogged everyone else, but the real picture is much more nuanced. From a hardware and platform perspective, it looks incredibly promising. Time will tell, but I'm optimistic that if 1X is willing to ship it, it must be robust enough. Kudos to them for this effort! However, from an AI (Robot Learning) standpoint, based on everything I've seen, the robot isn't quite there yet—definitely nothing that other industry or research labs can't do. But the playbook is clear: deploy robots in homes, collect more data, and continuously improve the model, much like the Tesla Autonomy flywheel. I really liked the raw review by Joanna Stern (@JoannaStern) from The Wall Street Journal (@WSJ). As said in the article, "the next few years isn't about owning a super useful robot, it's about raising one." Couldn't agree more. Link to the WSJ review is below 👇