In this paper, we present a diabolo model that can be used for training agents in simulation to play diabolo, as well as running it on a real dual robot arm system. We first derive an analytical model of the diabolo-string system and compare its accuracy using data recorded via motion capture, which we release as a public dataset of skilled play with diabolos of different dynamics. We show that our model outperforms a deep-learning-based predictor, both in terms of precision and physically consistent behavior. Next, we describe a method based on optimal control to generate robot trajectories that produce the desired diabolo trajectory, as well as a system to transform higher-level actions into robot motions. Finally, we test our method on a real robot system by playing the diabolo, and throwing it to and catching it from a human player.
@article{arxiv.2011.09068,
title = {An analytical diabolo model for robotic learning and control},
author = {Felix von Drigalski and Devwrat Joshi and Takayuki Murooka and Kazutoshi Tanaka and Masashi Hamaya and Yoshihisa Ijiri},
journal= {arXiv preprint arXiv:2011.09068},
year = {2020}
}