Learning a Shape-Adaptive Assist-As-Needed Rehabilitation Policy from Therapist-Informed Input

Abstract

Therapist-in-the-loop robotic rehabilitation has shown the promise to enhance rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, its broader adoption is limited due to insufficient interaction and limited adaptation capability. This article proposes a novel telerobotics-mediated framework that enables therapists to deliver assist-as-needed~(AAN) therapy based on two primary contributions. First, the reference motion for movement therapy is generated to encourage the active participant of patients based on their motion preferences encoded using a probabilistic model. Second, the telerobotics-mediated system enable the therapist to inform the via-points, enabling minimal but effective assistance for AAN therapy by partially deforming the reference motion. The effectiveness of the proposed strategy was validated a telerobotic system through two representative rehabilitation tasks, demonstrating its potential for remote AAN therapy.