四足机器人通过预测球旋转实现快速精准击球
Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
- 用视觉+物理模型预测球速与旋转，生成轨迹
- 新式模型预测控制让机器人自动生成多种击球策略
- 实机验证可对人类连续对打，适应不同旋转
开发能媲美人类速度、精度并应对各种球旋转的乒乓球机器人仍是腿式机器人的重大挑战。本文提出一套动态乒乓球系统，集成高速感知、轨迹预测与灵巧控制。系统利用外部摄像头实现高速球定位，结合带学习残差的物理模型推断球的旋转并预测轨迹，并采用新型模型预测控制（MPC）实现全身敏捷控制。值得注意的是，该控制范式可自动从不同回球目标中涌现出一系列连续击球策略。我们在Spot四足机器人上进行了真实世界演示，评估了各模块的精度，并展示了系统在应对不同旋转球时的协调能力。进一步验证显示，系统可与人类玩家进行连续对打。
原文摘要 · Abstract (English)
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and predict trajectories, and a novel model predictive control (MPC) formulation for agile full-body control. Notably, a continuous set of stroke strategies emerge automatically from different ball return objectives using this control paradigm. We demonstrate our system in the real world on a Spot quadruped, evaluate accuracy of each system component, and exhibit coordination through the system's ability to aim and return balls with varying spin types. As a further demonstration, the system is able to rally with human players.
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