Gaze-Assisted Adaptive Motion Scaling Optimization Using Graded and Preference Based Bayesian Approaches

A key component to the success of master-slave surgical systems is the quality of the master interface used to relay the surgeon's instructions to the slave robot. In previous work the authors developed a gaze-assisted intention recognition scheme, allowing the system to dynamically adapt the motion scaling based on where the user is trying to reach. This allowed users to perform tasks significantly more quickly and with less need for clutching. However, the system possessed a number of core parameters that were manually optimized, potentially providing a non-optimal solution depending on the user. This paper presents a Bayesian approach to the problem of optimizing a human-robot interface in a user-specific manner. Two Bayesian optimization methods are studied: one in which users are asked to grade robot behavior for a given set of parameters, and one where only preference relative to other parameter sets is expressed. The performance of these optimizations is evaluated in a blind comparison user study, demonstrating that the optimized parameters are preferred to the manually optimized ones in over 90 % of cases after only 12 test samples. These parameters are further shown to perform at least as well as the manually optimized ones in all cases, and showing statistically significant improvement in the case of the graded optimization.