A user model for adaptation of task parameters in robot-assisted exercise

Abstract

Robot-assisted exercises often use controllers which automatically adapt task parameters to the user’s performance. One problem with these controllers is how to accommodate the wide variety of degrees of impairment and to properly track the user’s improvement in spite of the inherent variability of performance that is typical of these tasks. Here we describe an adaptive controller model which uses reinforcement learning to maintain a model of user’s performance and uses it to continuously regulate the task parameter. We show that the model rapidly identifies the user’s model parameters and then smoothly tracks performance improvements.