A physics-based Juggling Simulation using Reinforcement Learning

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.