Abstract

We present DyDexHandover, a novel framework that employs multi-agent reinforcement learning to train an end-to-end RGB-based policy for bimanual object throwing and catching. To achieve more human-like behavior, the throwing policy is guided by a human-policy regularization scheme, encouraging fluid and natural motion, and enhancing the policy’s generalization capability.