Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this paper, we design a system with two multi-finger hands attached to robot arms to solve this problem. We train our system using Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple novel algorithm designs including learning a trajectory prediction model for the object. Such a model can help the robot catcher has a real-time estimation of where the object will be heading, and then react accordingly. We conduct our experiments with multiple objects in the real-world system, and show significant improvements over multiple baselines. Our project page is available at \url{https://binghao-huang.github.io/dynamic_handover/}.

翻译：人类无时无刻不在进行抛接物体的动作。然而，这种看似寻常的技能对机器人而言却充满挑战：机器人需要在高速状态下完成此类动态操作，实现精确协同并处理多样化物体。本文设计了一套由两只多指手爪固定在机械臂上构成的系统来解决该问题。我们通过多智能体强化学习在仿真环境中训练该系统，并运用仿真到现实迁移技术将其部署至真实机器人。为克服仿真与现实之间的差距，我们提出了多种创新算法设计，包括为物体学习轨迹预测模型。该模型能帮助机械手实时估算物体的运动方向并作出相应反应。我们在真实系统中使用多种物体进行实验，结果表明该方法相较于多个基准方案具有显著优势。项目页面详见\url{https://binghao-huang.github.io/dynamic_handover/}。