🚨 Attention, Robot Learning community! For the first time, @corl_conf is introducing a Call for Late-Breaking Results, focusing on Agentic Robotics and Real2Sim2Real! Research is moving fast, and we want to make room for important new results as they emerge! 📅 Deadline: October 26 #CoRL2026 🔗 corl.org/contributions/call-…

Robot learning has a data problem: the internet contains billions of human manipulation videos, but most robot datasets cover only a tiny fraction of real-world behavior. RoboTok turns those videos into a searchable source of dexterous manipulation demonstrations. Give it one human demonstration, and it retrieves other videos with similar hand movements.

🚨 Call for live demo / paper at CoRL 2026 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽! No paper required for demo track. Due Sep 27. 🔮 The real magical power of agent is live interactive demo with the audience, which is why we provide all-out support: - Robot, compute, and API are ready for you in the conference room - Custom robot / sim demo also welcomed - Just submit a video proof - friendly to industrial participants agentic-robotics-workshop.gi… Paper submission also welcomed! We also have an amazing speaker lineup from CMU, UC Berkeley, DeepMind, NVIDIA, and Tencent.

Just over a month later, I have now joined @physical_int full time! Wrote a bit about why I'm excited to research simulation at Pi stoneztao.substack.com/joini…

LeWM predicts action effects, but searches 9,000 sequences to act. Introducing INTACT: an end-to-end unified JEPA that completes the other half of LeWM: intent to action. Only 1 epoch. 0 search. 95.33% Direct macro SR. 2.9-5.5 ms inference. ~300× lower planning latency.

We just hit a weird milestone: our model became more reliable than your average home WiFi. Just like everybody else, we thought cloud inference was the obvious choice. Yet 2 days into the ACT-2 eval, our mind completely changed. If our hero @ArpitKalla didn’t cook, this video wouldn’t exist 🧵

Introducing ACT-2 Preview The first robotics model to unify broad generalization with high reliability. A single fine-tuning example can teach Memo a new behavior that generalizes. Zero shot, real unseen homes, 99% success rate.

A year ago, when I left Tesla Optimus to co-found Mondo, I made a promise to my son: I would build him a little robot buddy — something like the robot friends from our bedtime stories, but real. At the time, it was not really a product statement. It was a father’s promise to a little boy I love more than anything — a boy who loved stories about a child and his robot companion. Over the past year, that promise slowly became real — Beni the robot. Beni follows you, films you in 4K, moves across different terrain, jumps over obstacles, captures moments from new angles, and helps turn your clips into highlights. We designed it for families, pets, creators, athletes, and anyone who wants a robot that feels less like a gadget and more like a little sidekick. I’m deeply grateful to the Mondo team for turning countless prototypes, failures, late-night tests, and difficult tradeoffs into something people can finally meet. Although we have only just launched on Kickstarter, this world-class team is already deep in the “production hell”, and we expect to ship the product in the next few months. I’m also especially thankful to my family. Building hardware takes time, patience, and sacrifice from the people closest to you. Beni would not exist without their love and support. My son often visits our office and plays with Beni. Watching them together has reminded me why I started this journey in the first place. I do not want him to grow up thinking of robots only as distant machines in labs, factories, or videos. I want him to grow up in a world where robots can feel close, friendly, safe, and present — something he can play with, trust, and remember as part of his childhood. For the robotics community: Beni may look cute, but it is a serious robotics product. We used reinforcement learning to train Beni’s motion policies, with NVIDIA Isaac Lab and Google MuJoCo as part of our training and validation pipeline. Under the playful exterior are real robotics problems in locomotion, perception, control, and sim-to-real transfer. The lessons we are learning from Beni also directly inform our future humanoid product. Different form factor, same belief: advanced robotics should not feel distant or abstract. It should become close, present, and part of everyday life. For my son, Beni began as a promise. For Mondo Robotics, it is just the first step. Meet Beni: mondorobotics.com/

today in “things I didn’t expect people to run in my sim”: sheep getting herded by XLerobot robots @VectorWang2