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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.
Persistent object tokens enable humanoid robots to achieve a 71/80 success rate in complex loco-manipulation tasks, significantly outpacing previous benchmarks.
EgoPriMo enables humanoid robots to generate and forecast complex motions interactively using just egocentric observations and high-level language prompts.
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.
Achieve robust humanoid task execution in complex environments by turning high-level language instructions into verifiable, geometrically-grounded task programs that can recover from failures.
