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Explore 5 articles on Skills and applications from How Robots Learn.
Uni-PNP learns a unified object-pose-reaching policy by modeling contact-induced motion and progressing from graspable to ungraspable objects during reinforcement learning. Its authors report gains on seen and unseen objects, while providing only an unspecified claim of real-world transfer in the available abstract.
Published
A reinforcement-learning approach coordinates an anthropomorphic hand’s unequal fingers for self-support and locomotion without changing their design. The authors separately report simulated speed gains and physical demonstrations of untethered movement, recovery, keyboard input and visually guided object pushing.
Written
The method preserves where and when fingers contact an object instead of copying human joint motion directly. Its authors combine simulated physics refinement, morphology-independent contact retargeting and residual policy learning, then report deployment on four physical bimanual tasks without real-robot training data.
Written
Touch2Robot reconstructs and displays how a target robot hand would contact an object during human data collection. In four real-world tasks, its authors report improved replay completion, shorter collection time per successful demonstration, partial agreement with robot tactile measurements and stronger downstream Diffusion Policy performance than with visual-only feedback.
Written
GraspTwin combines a task-aware visual proposal with physics-based local grasp optimization. Its reported results distinguish simulation from a small physical test.
Written