Motion Planning for Throwing Manipulation Using Bayesian Optimization

Abstract

Throwing manipulation utilizes virtual driving forces, such as gravity, centrifugal force, and Coriolis force, to extend the workspace and increase the degrees of freedom available for manipulation. However, it is highly sensitive to various uncertainties such as wind disturbance and measurement noise, thereby deteriorating the throwing performance. This study proposes a motion planning method that uses Bayesian optimization to obtain an optimal arm trajectory for throwing manipulation through repeated trials. Bayesian optimization can explicitly account for stochastic uncertainties and obtain an optimal solution with a small number of trials. The key contributions of the proposed method are the explicit modeling of stochastic uncertainties using a Gaussian distribution and the ability to reduce the number of retraining attempts. The efficacy of the proposed motion planning method was validated through extensive experiments. Specifically, in environments with randomly changing wind directions and wind speeds, experiments demonstrated that the proposed method generated throwing motions that were more robust against wind disturbances than conventional methods based on iterative learning methods. Furthermore, even when the throwing target point is changed, the experiments demonstrate that the proposed learning method can learn with fewer trials than the conventional method by utilizing past observation data.