Dexterous-hand retargeting drops per-object training: one residual policy grasps unseen objects zero-shot
Turning human demonstrations into physically feasible dexterous-hand trajectories used to need per-object training or costly sampling optimization; adding a localized contact-error signal to the observation space lets one residual policy grasp unseen objects zero-shot at 60 to 350 times less compute than sampling methods.