Adaptive regulation of assistance 'as needed' in robot-assisted motor skill learning and neuro-rehabilitation

We propose a general adaptive procedure to select the appropriate degree of assistance based on a Bayesian mechanism used to estimate psychophysical thresholds. This technique does not need an accurate model of learning and recovery processes. This procedure is validated in the context of a motor skill learning problem (control of a virtual object), in which the controller is used to gradually increase task difficulty as learning proceeds. These automatic adjustments of task difficulty or the degree of assistance can be used to promote not only motor skill learning but also neuromotor recovery.