supervised-robot-learning.gi… One of the best intros I always recommend ppl new to robot learning to watch. @notmahi is my robot learning 101 professor.

We can use videos from the internet to teach robots! Do as I Do, from @bhawna_paliwal_ , @HarithejaE , and Willian Liang at UC Berkeley — advised by @pabbeel, @notmahi, and @JitendraMalikCV, used an algorithm that reconstructs hand-object interactions from monocular RGB video and retargets them into real, executable trajectories for multi-fingered dexterous hands. Just using "low quality" video footage of humans doing tasks. No sensors. The Sharpa Wave robot hand being anthropomorphic, it matches human kinematics. Not only that works, but at fast speed, too! Congrats to the team, that's super exciting! Project: do-as-i-do.com/ #Robotics #SharpaWave #Sharpa #EmbodiedAI #DexterousManipulation #RobotLearning

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

Introducing Do as I Do 👀, a framework to transform everyday human videos into 100s of dexterous robot demos. Co-led with @bhawna_paliwal_ and @HarithejaE, and check out @notmahi's thread! Here’s a little preview of our dexterous manipulation results. More about how we produce them from human reconstructions in this mini-thread! 🧵 x.com/notmahi/status/2067640…

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

Robots are the bottleneck in scaling robotics, and learning from human video promises to solve it. But how can chaotic human data ever measure up to sanitized, lab-made teleoperation data? Introducing Do as I Do: establishing a much needed correspondence between human videos and dexterous robot data. Some fun insights below: 🧵

Introducing Human Universal Grasping (HUG): dexterous grasping learned entirely from human hands, with zero robot data. 🌐 Website: grasping.io 📄 Paper: arxiv.org/abs/2606.17054 💻 Code: github.com/KevinyWu/hug

We are open-sourcing Wall-OSS-0.5. Pretrain Once, Act Anywhere. Wall-OSS-0.5 is a VLA model for real-world robotic manipulation, exploring whether pretraining alone can produce robot capabilities directly testable on physical hardware before task-specific fine-tuning. Key technical highlights: • Gradient-bridged co-training • Vision-Aligned RVQ Action Tokenizer • Action-Space Supervision • DMuon distributed optimizer In zero-shot real-robot evaluation, the pretrained checkpoint achieved task-progress scores above 80 on multiple tasks, including Block Sorting, Fruit Sorting, Ring Stacking, and Rope Tightening. Paper, code, blog, and uncut videos: x2robot.com/oss#resources

I want to offer some unsolicited advice to computer vision researchers jumping into robotics. Don't focus too much on VLMs, VLAs etc. That's fine, but the real action is at the sensorimotor level. Most of the open problems in robotics are in manipulation, which is about hand-object interaction, and contacts and forces are central. Proprioception and tactile sensing are as important as vision. Don't get seduced by cherry-picked demos. You can't do robotics without doing robotics.