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Qualitative examples show the PhysBrain 1.5 model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.
Human-as-Humanoid achieves a staggering 4.8–7.2x increase in demonstration throughput, transforming how humanoid robots learn from human actions.
Onboard perception and pre-trained whole-body control can enable humanoid robots to perform dynamic racket sports like tennis without external motion capture or task-specific retraining.
