We trained a robot to play football. How? Self-play for 140 years in a virtual World Cup. Meet the #Messinator:

Evolving dexterity with GPT-6 Astra 🖐️ Been trying Astra recently. Its zero-shot dexterous manipulation is already quite surprising. More interesting is seeing it learn and improve through simulation training, from pen spinning and Rubik's Cube to hammer use. The real goal would be to evolve this dexterity in the real world. github.com/jianglongye/dexte…

A humanoid on the monkey bars! 🐒 The robot from the @leggedrobotics Lab at ETH Zürich jumps up to the structure, swings across it, and drops down to a landing. Most legged robots build a terrain map first, and a heightfield is the wrong representation for a horizontal bar with air above and below it. Thin overhanging geometry is exactly what a map throws away. So the policy reads the raw scan from a head-mounted solid-state lidar, with no reconstruction step in between. An attention-based encoder picks out which of the sparse returns matter, and a GRU carries memory through the sequence, which counts when the geometry you're reaching for slides out of the sensor's view. Training is a phase-scheduled teacher-student setup. Separate privileged experts for jumping up, brachiating and jumping down, each with its own curriculum, and a scheduler handing control between them. The student is then distilled through DAgger, a critic warm-up, and PPO with the behaviour-cloning anchor decaying as reward takes over. The detail I enjoyed most sits in the sim-to-real section. Alongside LiDAR noise, they modeled battery voltage sag and actuator thermal limits. 🔗 Here's the paper page: nemantor.github.io/sparse-3d… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

A new Science #Robotics study highlights how a super-scaled motion tracking model trained on more than 100 million frames of data enables natural, whole-body movements in humanoid robots. scim.ag/3TYYKlp

Try to always be productive. Not busy. Not distracted. Productive.