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By jointly modeling hand-object motion, WHOLE overcomes occlusion and out-of-sight challenges in egocentric videos, achieving state-of-the-art results in hand and object pose estimation.
A single RL policy trained on procedurally generated tools in simulation can achieve zero-shot dexterous manipulation of diverse real-world tools, rivaling task-specific policies.
Humanoid robots can now perform vision-based parkour, chaining together dynamic skills like climbing, vaulting, and rolling, adapting to real-time obstacle changes.
