Vision-based model predictive control for within-hand precision manipulation with underactuated grippers

Precision manipulation with underactuated hands is a challenging problem due to difficulties in obtaining precise gripper, object and contact models. Using vision feedback provides a degree of robustness to modeling inaccuracies, but conventional visual servoing schemes may suffer from performance degradation if inaccuracies are large and/or unmodeled phenomena (e.g. friction) have significant effect on the system. In this paper, we propose the use of Model Predictive Control (MPC) framework within a visual servoing scheme to achieve high performance precision manipulation even with very rough models of the manipulation process. With experiments using step and periodic reference signals (in total 204 experiments), we show that the utilization of MPC provides superior performance in terms of accuracy and efficiency comparing to the conventional visual servoing methods.