Robots can teach people how to move their arm

We describe a new theoretical framework for robot-aided training of arm movements. This framework is based on recent studies of motor adaptation in human subjects and on general considerations about adaptive control of artificial and biological systems. We propose to take advantage of the adaptive processes through which subjects, when exposed to a perturbing field, develop an internal model of the field as a relation between experienced limb states and forces. The problem of teaching new movements is then reduced to the problem of designing force fields capable of inducing the desired movements as after-effects of the adaptation triggered by prolonged exposure to the fields. This approach is an alternative to more standard training methods based on the explicit specification of the desired movement to the learner. Unlike these methods, the adaptive process does not require explicit awareness of the desired movement as adaptation is uniquely concerned with restoring a preexisting kinematic pattern after a change in dynamical environment.