A physics-based Juggling Simulation using Reinforcement Learning

A physics-based Juggling Simulation
using Reinforcement Learning

Jason Chemin1    Jehee Lee1

1 Seoul National University   

The 2D character learns how to catch and throw a ball.
Then, it combines both tasks to perform toss juggling.

Juggling is a physical skill which consists in keeping one or several objects in continuous motion in the air by tossing and catching it. Jugglers need a high dexterity to control their throws and catches which require speed, accuracy and synchronization. The more balls we juggle with, the more these qualities have to be strong to achieve this performance. This complex skill is good challenge for realis- tic physical based simulation which could be useful for jugglers to evaluate the feasibility of their tricks. This simulation has to understand the different notations used in juggling and to apply the mathematical theory of juggling to reproduce it. In this paper, we present a deep reinforcement learning method for both tasks catching and throwing, and we combine them to recreate the all juggling process. Our character is able to react accurately and with enough speed and power to juggle with up to 7 balls, even with external forces applied on it.


Jason Chemin and Jehee Lee. 2018.
A physics-based Juggling Simulation using Reinforcement Learning.
ACM - MIG 2018

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This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the SW STARLab support program (IITP-2017-0536-20170040) supervised by the IITP(Institute for Information communications Technology Promotion).