动态交接：双臂手的抛掷与接住
Dynamic Handover: Throw and Catch with Bimanual Hands
AI总结：
本文提出一种基于双臂多指手的动态抛接系统，通过多智能体强化学习在仿真中训练并引入轨迹预测模型等新算法设计实现Sim2Real迁移，在真实世界实验中显著超越多个基线。
摘要：
人类经常抛掷和接住物体。然而，这种看似常见的技能给机器人实现带来了许多挑战：机器人需要高速执行此类动态动作、精确协作，并与各种物体交互。在本文中，我们设计了一个由两只安装在机械臂上的多指手组成的系统来解决这一问题。我们在仿真中使用多智能体强化学习（MARL）训练系统，并执行Sim2Real迁移以部署到真实机器人上。为克服Sim2Real差距，我们提供了多种新颖的算法设计，包括为物体学习一个轨迹预测模型。该模型能帮助机器人接手实时估计物体的去向，并做出相应反应。我们在真实世界系统中使用多种物体进行了实验，并相较于多个基线取得了显著提升。我们的项目页面位于\url{https://binghao-huang.github.io/dynamic_handover/}。
英文摘要：
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/}.
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。
