Publikation
Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling
Jonathan Rainer Lippert; Kai Ploeger; Abir Chowdhury; Hermann Müller; Jan Peters; Alap Kshirsagar
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2607.15129, Pages 1-7, arXiv, 2026.
Zusammenfassung
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 archi-
tecture 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 demon-
strate 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