Big thing
Boston Dynamics gives Atlas four-finger hands
13 degrees of freedom (up from 7), no pinky, a motor in every joint and touch sensors, built for tool use and sim-to-real training, with a target of 100K hands a year.
Taken as a step change. @AutismCapital: "It's over boys. We had a good run" (143K). r/accelerate's top reply: "Hoooooo boy lol shit is gonna get wild in the next year."
The drill clip set off the "it's over" crowd, and the people counting joints said Chinese hands already have more. We're with the excited side. The news is where Boston Dynamics thinks robot skills will come from. Rivals copy the human hand joint for joint so they can learn from people in data gloves and motion-capture suits. Boston Dynamics built a simpler hand that's easy to simulate exactly, so Atlas can learn by practising in simulation, and we hope that bet wins, because then skills grow with compute instead of with hours of people showing robots what to do.
Labs news
1. Tesla halves AI5's memory to build enough Optimus brains
Musk says AI5 drops to 72GB and AI6 to 144GB, the only way to get enough volume for Optimus. Bandwidth stays the same, so he expects a negligible hit.
@MilkRoadAI: "there isn't enough supply to get the volume Tesla needs for Optimus. I genuinely don't own enough $MU." @niccruzpatane puts the saving at $1,100 to $1,600 per robot.
Heat: high · 1.6M views, 19 posts
2. Clone shows its Torso 3 hand, with Torso 4 due in November
A teleoperated demo of its muscle-and-tendon hand, the last of its legacy designs. Torso 4 targets two-handed tasks at fixed workstations.
@humanoidsdaily on the two hands shown hours apart: "Boston Dynamics built Atlas's like a machine" and "Clone built its like a body." @XRoboHub: Clone redesigned the whole system "to make it easier to simulate and control."
Heat: medium · 241K views, 9 posts
3. UBTECH says its factory could build a humanoid every 8 to 10 minutes
CEO James Zhou's factory tour claims 1,500 units a month at full capacity. That's capacity, not confirmed output.
Doubts. @ErenChenAI: the CFO expects 1,500 to 2,000 deliveries in 2026, roughly 11 to 15% of 13,361 preorders. @clankrmedia: "Still, those are pretty good numbers, right?"
Heat: low · 4.6K views, 6 posts
4. Astribot's T1 goes on sale from $18K
Launched at IROS: a cable-driven robot with 23 degrees of freedom, up to 5kg per arm, full SDK access and Astribot's own Lumo-2 model.
Few takes yet. @XRoboHub compares it with Agility's Digit 5 at around $200,000 and 1X NEO at $20,000.
Heat: low · 17K views, 1 post
Key research
PROWL-2: agents that repair their own world model
Odyssey's agents find where their world model's imagination goes wrong and keep that imagined experience out of their training: on Gate-3 (three simulated quadrupeds), success rose from about 30% to 70% (@TheHumanoidLabs), and the paper credits almost all of it to that gate (repair alone reached about 35%). It matters because a world model can still help, but only while the real environment keeps checking what it imagines.
γ₀: one motion policy for 200+ robot bodies
A generalist RL policy for motion control, trained across millions of randomized bodies derived from more than 200 robot models. It matters because one controller that fits any body would end per-robot retraining.
LATENT: humanoid tennis from messy human motion data
Tsinghua and Galbot's March paper, now winner of IROS 2026's Best Entertainment and Amusement Paper award, turns imperfect human motion data into real-world tennis rallies on a Unitree G1 humanoid. It matters because clean motion capture is scarce, and noisy human data is everywhere.
Odyssey's jump came almost entirely from keeping its agents from learning on whatever the world model gets wrong, and training on everything it imagined left them worse than where they started. So a world model only helps while the real environment keeps checking it, and for robots that means robot time is still the bottleneck. A bit disappointing: we wanted imagination to get robots past the data wall faster than this.