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Briefings and ideas about Humanoid robots from AI podcast discussions and the research they cite, with links to the original sources.
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.
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.
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.
Periodic Labs' Liam Fedus says the startup puts AI on lab instruments to capture richer data and automates bottlenecks one at a time. He calls full autonomy a non-goal and says humanoid robots would be a slower path to its aims.
Keerthana Gopalakrishnan, research lead for Gemini Robotics at Google DeepMind, argues that people won't use unsafe robots. She splits robot safety into avoiding accidents, following intent within guardrails and handling sensor failures.
Google DeepMind's Keerthana Gopalakrishnan said humanoid sprint records show locomotion that trains well in simulation. In her view, contact-heavy hand work is the real frontier, where hands have improved but still need reliability and lower cost.
Keerthana Gopalakrishnan of Google DeepMind says robot models are at a GPT-2 stage. In her view, a GPT-3 moment needs few-shot teaching to work across many tasks and one brain to run reliably on a new robot body, a test language models never faced.
Keerthana Gopalakrishnan, who leads Gemini Robotics research at Google DeepMind, explained how a reasoning model open to developers directs an action model limited to testers, and where she says failures arise.
Palmer Luckey argues humanoid robots may suit civilian work, but defense needs specialized machines. He says Anduril would rather use other companies' humanoids, for example to run costly legacy radars.
Figure says its Helix 2.5 robot did chores in 30 unseen homes, with human-video pretraining lifting full-task success to 56% from 9%, though each chore was trained elsewhere. Robinhood CEO Vlad Tenev said he wouldn't want one at home.