DyDexHandover：基于仅RGB感知的类人双臂动态灵巧交接
DyDexHandover: Human-like Bimanual Dynamic Dexterous Handover using RGB-only Perception
AI总结：
DyDexHandover提出一种基于RGB感知的多智能体强化学习框架，实现双臂空中投掷与接住的类人动态交接，在训练和未见物体上分别达到99%和75%的成功率。
摘要：
空中动态交接是双臂机器人的一个基本挑战，需要精确的感知、协调和自然的运动。先前的方法通常依赖动力学模型、强先验或深度传感，限制了泛化性和自然性。我们提出DyDexHandover，一个新颖的框架，采用多智能体强化学习训练一个端到端的基于RGB的策略，用于双臂物体投掷和接住。为了实现更类人的行为，投掷策略由一个人体策略正则化方案引导，鼓励流畅自然的运动，并增强策略的泛化能力。在Isaac Sim中构建了一个双臂仿真环境用于实验评估。DyDexHandover在训练物体上达到近99%的成功率，在未见物体上达到75%，同时生成类人的投掷和接住行为。据我们所知，这是首个仅利用原始RGB感知实现双臂空中交接的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
