Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling

2026-07-16

AI Summary

This work addresses the challenges of perceptual uncertainty, temporal synchronization, and contact interaction inherent in dynamic object exchange during human–robot collaborative juggling. The authors propose a real-time planning and control framework that integrates predictive ball trajectory tracking, online trajectory optimization based on a multiple-shooting method, state machine–driven coordination logic, and feedback control. This system enables, for the first time, users of varying skill levels to participate in triadic three-ball cascade juggling with a robot, significantly enhancing collaborative robustness and continuity. In experiments, all eight participants surpassed prior performance benchmarks within ten minutes; one user achieved a record of 20 consecutive successful catches in the three-ball cascade, while another attained 40 flawless catches with 100% success in a single-ball task.

Abstract

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

Problem

• human-robot interaction
• dynamic object exchange
• partner juggling
• real-time coordination
• motion variability

Innovation

• human-robot juggling
• real-time planning
• adaptive trajectory optimization
• multiple-shooting
• predictive tracking