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arXiv · 2509.17350

DyDexHandover: Human-like Bimanual Dynamic Dexterous Handover using RGB-only Perception

Haoran Zhou·Yangwei You·Shuaijun Wang
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Abstract

Dynamic in air handover is a fundamental challenge for dual-arm robots, requiring accurate perception, precise coordination, and natural motion. Prior methods often rely on dynamics models, strong priors, or depth sensing, limiting generalization and naturalness. 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 generalization capability of the policy. A dual arm simulation environment was built in Isaac Sim for experimental evaluation. DyDexHandover achieves nearly 99 percent success on training objects and 75 percent on unseen objects, while generating human-like throwing and catching behaviors. To our knowledge, it is the first method to realize dual-arm in-air handover using only raw RGB perception.

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Haoran Zhou, Yangwei You, Shuaijun Wang. 2025-09-22. DyDexHandover: Human-like Bimanual Dynamic Dexterous Handover using RGB-only Perception. https://arxiv.org/abs/2509.17350

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