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Reporting and discussion about Robots & Autonomy, with links to the original sources.
Josh Gao, CEO of robot-data supplier Mecka AI, says labs train on about a million hours of human data, tens of thousands of gripper hours and a few teleop hours. He argues scaling that alone won't yield a robot that learns any task by watching.
Mecka CEO Josh Gao says robotics is still orders of magnitude short of Tesla's driving data. He expects constrained, goal-oriented jobs such as industrial cleaning to come first. He says homes are where the field must take the most care, and that data collected on site can close the reliability gap.
Mecka CEO Josh Gao described how his company makes robot training data, with camera caps, force-sensing gloves and checks against fraud. He said contributor pay matters less to its margin than verifiably high-quality data at scale.
On Moonshots, Salim Ismail argued robots will start in dull, dirty and dangerous work and reach homes later; Peter Diamandis expects them to move past that within a year, if the dexterity and capability the panel discussed are really achieved. Anousheh Ansari said safety comes first and doubted the humanoid form.
Peter Diamandis cited a fall from $2.36 to $0.38 per robot subtask between July and September and projected near-zero cost within a year. RoboCurve's published test instead compares two models at $1.14 and $0.90 per attempt.
Peter Diamandis described a Tesla Roadster demo in which suction fans held the car upside down on a ramp. A presentation clip he played described compressed-air thrusters borrowed from SpaceX for its hops. His Moonshots guest Salim Ismail doubted any insurer would cover it and said regulators always lag new technology.
Atomic Machines says its Matter Compiler builds tiny devices from code, starting with a fast power relay. On Moonshots, the hosts argued over how self-copying factories might follow. Those are their forecasts; none has been shown.
Jason Ma, Dyna Robotics' co-founder and chief scientist, says the company must own its robot hardware because of latency, human-data transfer and long supply chains. He also says wheels beat legs for now.
Dyna Robotics chief scientist Jason Ma argues that robots should predict future camera video, not only their next moves. He says the bet could fail if touch or sound makes rival models far more data-efficient.
Jason Ma, chief scientist at Dyna Robotics, says robotics has no internet of data. Dyna pretrains on a million hours of human video, adapts each task with minutes to hours of robot data, and Ma expects synthetic environments to add more.
Jason Ma, chief scientist of Dyna Robotics, says robots fold thousands of napkins daily across many Din Tai Fung locations in the US. He contrasts that with one-site pilots and says Dyna aims for hundreds of robots next year.
Dyna Robotics' Jason Ma says hour-long robot jobs such as laundry need a planning layer around the low-level action model. He names fragile robot hands, slow hardware testing and vision-only data as limits on more general work.