接住、抛出、重复：人机搭档杂耍的规划
Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling
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中文总结 AI 辅助
研究人机搭档杂耍问题，提出实时规划和控制架构，集成多种技术实现同步多球杂耍。通过用户研究，证明系统能达成三球级联共享，参与者成绩超此前最佳，提升了人机物理交互和共享自主性。
中文摘要 AI 辅助
humans和机器人之间的动态物体交换仍然是一个具有挑战性的问题，因为感知、时间安排和丰富接触的交互存在不确定性。人机杂耍是这个问题特别苛刻的一个实例，需要精确的实时协调、带有反馈控制的预测性运动规划以及对人类运动变化的鲁棒性。我们提出了一种用于人机搭档杂耍的实时规划和控制架构，该架构使机器人能够与人类搭档以同步多球模式可靠地接住和抛出球。该系统集成了预测性球跟踪、使用多射击公式的自适应在线轨迹优化以及基于状态机的协调逻辑，以实现同步多球人机搭档杂耍。在一项针对8名从初学者到专家的不同杂耍技能参与者的用户研究中，我们证明了我们的系统可以实现机器人和人类之间共享的三球级联。所有参与者在10分钟的测试环节内都超过了之前报告的最佳结果，一名参与者将之前共享三球级联杂耍的记录延长了五倍，达到连续20次机器人接球，另一名参与者在单球接球和返回设置中连续40次接球成功率达到100%。视频文档可在这个https网址找到。
英文摘要
Dynamic object exchange between humans and robots remains a challenging problem due to uncertainty in perception, timing, and contact-rich interaction. Human-robot juggling represents a particularly demanding instance of this problem, requiring precise real-time coordination, predictive motion planning with feedback control, and robustness to variability in human motion. Enabling such skills is of interest for advancing physical human-robot interaction and shared autonomy. We present a real-time planning and control architecture for human-robot partner juggling that enables a robot to reliably catch and throw balls in synchronized multi-ball patterns with a human partner. The system integrates predictive ball tracking, adaptive online trajectory optimization using a multiple-shooting formulation, and a state-machine-based coordination logic to enable synchronized multi-ball human-robot partner juggling. In a user study with 8 participants of varying juggling skill from beginner to expert, we demonstrate that our system can achieve three-ball cascades shared between the robot and the human. All participants exceeded previously reported best-case results within a 10-minute test session, with one participant extending the previous record for shared three-ball cascade juggling fivefold to 20 consecutive robot catches, and another participant achieving a 100% success rate with 40 consecutive catches in a single-ball catch-and-return setting. Video documentation can be found at https://kai-ploeger.com/partner-juggling
发表机构
- Technical University of Darmstadt（达姆施塔特工业大学）
- Justus-Liebig University Gießen（吉森尤斯 - 李比希大学）
- German Research Center for AI (DFKI)（德国人工智能研究中心（DFKI））
- Robotics Institute Germany（德国机器人研究所）
- Indian Institute of Technology Delhi - Abu Dhabi（印度理工学院德里分校 - 阿布扎比）
机构由 AI 辅助整理，请以论文原文为准。
