🤖 The Welder Checks Its Own Work
Good Morning, Roboticists!
A robot welds, grades itself, and fixes its mistakes. Meanwhile, humans still need three meetings to approve a spreadsheet.
The Welder Checks Its Own Work
TL;DR: Yaskawa and GAZOO Racing disclosed an autonomous welding system already working at the Gamagori GR Kart workshop since September. MOTOMAN NEXT recognizes frame conditions, adjusts welding, inspects results and makes corrections using images, welding sounds and process data. The announcement moves beyond a demonstration, although it provides no quantified defect rate, cycle time or savings for this production line. Read more →
LG Opens Its Robot Classroom
TL;DR: LG says its robotics data factory has begun partial operations while it prepares actuator manufacturing and seeks customers. On its earnings call, the company outlined a path through research, public-sector and industrial applications before household humanoids around 2030. It also wants appliances designed for robots, but has disclosed neither a consumer price nor a firm home-robot launch date. Read more →
IRON’s Battery Starts Its Ramp
TL;DR: XPENG vice president Gu Jie says IRON's solid-state battery has entered small-batch production, while the humanoid remains before its formal start of production. The update advances a component central to the robot's design without establishing manufacturing scale or practical endurance. Energy density, runtime, production volumes and detailed safety-test results remain undisclosed, and the exact production chemistry remains unclear. Read more →
Sales Rise, Research Eats The Margin
TL;DR: Unitree reported quarterly revenue of 665 million yuan, up roughly 70 percent, while attributable profit fell 17.4 percent to 112.7 million yuan. Research spending climbed to 127 million yuan as the company invested in embodied AI. The figures reveal a growing business with a heavier development bill; company-wide sales should not be presented as humanoid-only revenue or deployment profitability. Read more →
Fifteen Teams Face The Real Robot
TL;DR: Robbyant says fifteen teams will advance to a Shanghai final using AgileX's Cobot Magic in a 48-hour physical-robot hackathon. The November event tests adaptation beyond model development and simulation, following more than 1,800 team registrations. Finalists, scoring rules, tasks and advance hardware access remain unsettled, so registration numbers should not be mistaken for completed entries or demonstrated robot capability. Read more →
Fix The Move, Keep The Policy
TL;DR: Adaptive Residual Correction keeps an existing robot policy frozen and learns small adjustments where precision breaks down. Sparse human interventions guide when correction activates and which directions it explores. On a Miroki robot performing tray stacking, marker insertion and cup stacking, average success rose from 33.3 to 75 percent. The result concerns three controlled tasks, not general autonomous recovery. Read more →
The Gap Refuses To Stand Still
TL;DR: A Zurich team trained a quadrotor controller in differentiable simulation to fly through a moving narrow gap using onboard visual and inertial sensing. The policy transferred directly to hardware, clearing the gap in eight of ten physical trials. Its 99.3 percent simulation score is separate: real-world testing remains small, with perception noise and dynamics differences limiting transfer reliability. Read more →
Three Robots Share One World
TL;DR: World Dreamer v5-lite builds a shared spatial world state from multiple camera views while retaining separate action streams for each robot. Tests include cooperating LeKiwi platforms and mixed LeKiwi-OpenArm teams transferring blocks, unscrewing caps and bagging starfruit. Across four physical tasks, average success reached 78.8 percent versus 65 percent for the strongest evaluated baseline, without establishing reliability in open-ended workplaces. Read more →
Human Videos Teach The Missing Sequence
TL;DR: ThinkWorld trains robot action reasoning around predicted changes in the world, allowing human videos to contribute without robot action labels. On a bimanual YAM system, researchers tested sorting, color matching and cup hanging. An oven task combined robot demonstrations of individual skills with human videos of complete sequences, showing skill composition in controlled experiments rather than unrestricted learning from video. Read more →
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