Interactive priming enhanced by negative damping aids learning of an object manipulation task

Movement Strategy Discovery during Training via Haptic Guidance

Haptic guidance has previously been investigated to facilitate motor skill training, whereupon a robotic device assists a trainee in executing the desired movement. However, many studies have reported a null or even detrimental effect of haptic guidance on training compared to unassisted practice. While prior studies have focused on using haptic guidance to refine a movement strategy, our study explores its effect on the discovery of a new strategy. Subjects learned to manipulate a virtual under-actuated system via a haptic device either with or without haptic guidance (and without haptic feedback of system dynamics). The guidance enabled subjects to experience a range of successful movements, rather than strictly enforcing one trajectory. Subjects who trained with guidance adopted a strategy that involved faster reaches, required greater control of the system's degrees of freedom, and increased the potential for faster task completion. However, overall improvement of task performance was limited with the new strategy.

Breaking it down is better: haptic decomposition of complex movements aids in robot-assisted motor learning

Training with haptic guidance has been proposed as a technique for learning complex movements in rehabilitation and sports, but it is unclear how to best deliver guidance-based training. Here, we hypothesized that breaking down a complex movement, similar to a tennis backhand, into simpler parts and then using haptic feedback from a robotic exoskeleton would help the motor system learn the movement. We also examined how the particular form of the decomposition affected learning. Three groups of unimpaired participants trained with the target arm movement broken down in three ways: 1) elbow flexion/extension and the unified shoulder motion independently ("anatomical" decomposition), 2) three component shoulder motions in Euler coordinates and elbow flexion/extension ("Euler" decomposition), or 3) the motion of the tip of the elbow and motion of the hand with respect to the elbow, independently ("visual" decomposition). A control group practiced the same number of movements, but experienced the target motion only, achieving eight times more direct practice with this motion. Despite less experience with the target motion, part training was better, but only when the arm trajectory was decomposed into anatomical components. Varying robotic movement training to include practice of simpler, anatomically-isolated motions may enhance its efficacy.

Can robots help the learning of skilled actions?

Learning to move skillfully requires that the motor system adjusts muscle commands based on ongoing performance errors, a process influenced by the dynamics of the task being practiced. Recent experiments from our laboratories show how robotic devices can temporarily alter task dynamics in ways that contribute to the motor learning experience, suggesting possible applications in rehabilitation and sports training.