Ge Yang

Trained entirely in imagination, our policy matches a PPO policy trained in simulation on the same locomotion test (81.8% vs 80.7% command-tracking success), with 7.9× fewer simulator transitions and about half the training time. More results soon 🤫

@EkaRobotics has raised a $150M Series A. Generality v/s Performance is the fundamental tension at the frontier of robotics. Generality without performance is just a prototype. Performance without generality is just a single-purpose tool. Customers shouldn’t have to choose. Eka breaks the tradeoff. Powered by our Vision-Force-Action (VFA) model, we deliver the performance and versatility customers need to put robots to work every day. Eka is built on three convictions: ⚡️Force is the language of manipulation. 🚀 Reinforcement learning is how dexterous manipulation scales. 🎂 System 1—fast, reliable physical execution—is the bottleneck to solve. With this funding, we will scale our model and accelerate customer deployments. Grateful to be building alongside a dream team and investors who share our ambition.

2/9 To train it, we reset the simulator to a recorded hardware state, apply the same recorded commands, and learn command corrections so the simulated rollout matches the recorded hardware trajectory. We previously used UAN on a quadruped's arm (uan.csail.mit.edu). But motion with contact was tricky.

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…

Today, we present a step-change in robotic AI @sundayrobotics. Introducing ACT-1: A frontier robot foundation model trained on zero robot data. - Ultra long-horizon tasks - Zero-shot generalization - Advanced dexterity 🧵->

Today, we present a step-change in robotic AI @sundayrobotics. Introducing ACT-1: A frontier robot foundation model trained on zero robot data. - Ultra long-horizon tasks - Zero-shot generalization - Advanced dexterity 🧵->

Learning about active perception with @Haoyu_Xiong_ -- your robot needs a head, and to be able to control where it's looking, in order to perform complex tasks!

Many of today's data collection systems do not capture human perceptual behaviors. The observation mismatch—between what the human sees and what the robot learns from—hinders the learning of effective manipulation policies. To see what the robot sees, we developed a VR interface—Async Teleop, which introduces decoupled view rendering to reduce motion sickness 😵‍💫🥴🤢 — an issue that is insufficiently addressed in prior VR teleop systems. 3/7

We have been focusing on policy learning for robotics for a while. But can hardware be learned as well? Check out @yswhynot ‘s recent co-design work that learns what a soft gripper should be if we want to do better manipulation.