机器人精准投掷：硬件实测误差仅0.28米，成功率远超人类。
Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration
- 融合学习与模型控制，用高频残差策略提升末端精度。
- 6米外投掷平均误差0.28米，3-5米内命中率56.8%。
- 首次实现腿部机械臂投掷的量化验证，适合动态抓取研究者。
投掷是机器人扩展手臂操作范围的基础技能。本文提出一种结合学习与模型控制的全肢体投掷控制框架，适用于带腿移动操作臂。系统包含三个部分：末端执行器的基准跟踪策略、高频残差策略以增强跟踪精度，以及基于优化的末端加速度控制模块。在6米外投掷目标时，平均落地误差为0.28米。对比实验中，系统在3-5米范围内以6米/秒速度投掷随机小目标，速度跟踪误差为0.398米/秒，成功率达56.8%，而人类学生仅为15.2%。该工作首次在硬件上实现可量化的预握式投掷，推动了动态全肢体操作的发展。
原文摘要 · Abstract (English)
Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning and model-based control for prehensile whole-body throwing with legged mobile manipulators. Our framework consists of three components: a nominal tracking policy for the end-effector, a high-frequency residual policy to enhance tracking accuracy, and an optimization-based module to improve end-effector acceleration control. The proposed controller achieved the average of 0.28 m landing error when throwing at targets located 6 m away. Furthermore, in a comparative study with university students, the system achieved a velocity tracking error of 0.398 m/s and a success rate of 56.8%, hitting small targets randomly placed at distances of 3-5 m while throwing at a specified speed of 6 m/s. In contrast, humans have a success rate of only 15.2%. This work provides an early demonstration of prehensile throwing with quantified accuracy on hardware, contributing to progress in dynamic whole-body manipulation.
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