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VAIC enables humanoid robots to perform complex tasks in real-world settings without the need for perfect state observability, significantly advancing their practical deployment.
Robots can now learn fine-grained manipulation skills directly from human demonstrations, thanks to a new imitation learning framework that automatically figures out the right rewards and handles noisy real-world data.
Humanoid robots can now reliably perform long-horizon loco-manipulation tasks in the real world thanks to a novel root-trajectory conditioned policy and persistent object estimation.
