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

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

|Authors: Jonathan Rainer Lippert, Kai Ploeger, Abir Chowdhury, Hermann Müller, Jan Peters, Alap Kshirsagar

|Venue: arXiv preprint | Year: 2026

|arXiv: 2607.15129v1

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

Key Contributions

• Dynamic object exchange between humans and robots remains a challenging problem due to uncertaint…
• Human-robot juggling represents a particularly demanding instance of this problem, requiring prec…
• Enabling such skills is of interest for advancing physical human-robot interaction and shared aut…
• We present a real-time planning and control architecture for human-robot partner juggling that en…
• The system integrates predictive ball tracking, adaptive online trajectory optimization using a m…

Topics

• vision
• reinforcement-learning
• planning
• control
• human-robot-interaction

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Lippert2026_260715129v1,
  title     = {Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling},
  author    = {Jonathan Rainer Lippert and Kai Ploeger and Abir Chowdhury and Hermann Müller and Jan Peters and Alap Kshirsagar},
  year      = {2026},
  eprint    = {2607.15129v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2607.15129v1}
}