仅用彩色图像实现双臂自然交接，成功率超98%。
DyDexHandover: Human-like Bimanual Dynamic Dexterous Handover using RGB-only Perception
- 基于多智能体强化学习，端到端训练双臂抛接策略。
- 训练物体成功率99%，未见物体仍达75%。
- 首次纯RGB感知实现类人抛接行为，适合机器人交互研究。
动态空中交接是双臂机器人的基础挑战，需精准感知、协调动作与自然运动。以往方法常依赖动力学模型、强先验或深度传感，限制了泛化性与自然性。本文提出DyDexHandover，采用多智能体强化学习，训练端到端的基于RGB图像的双臂抛接策略。为实现更类人的行为，抛掷策略引入人类策略正则化，鼓励流畅自然动作，提升策略泛化能力。在Isaac Sim中构建双臂仿真环境进行评估。实验显示，该方法在训练物体上成功率接近99%，在未见过物体上达75%，同时生成类人抛接行为。据我们所知，这是首个仅使用原始RGB图像实现双臂空中交接的方法。
原文摘要 · Abstract (English)
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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