An analytical diabolo model for robotic learning and control
This work provides a more accurate and physically consistent model for diabolo dynamics, which is significant for robotics researchers developing control systems for complex manipulation tasks.
This paper introduces an analytical model for the diabolo-string system, demonstrating superior accuracy and physical consistency compared to a deep-learning predictor using motion capture data. The model was successfully applied to a real dual robot arm system, enabling it to play diabolo and interact with a human player.
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.