Therapist-in-the-loop robotic rehabilitation has shown great promise in enhancing rehabilitation outcomes by integrating the strengths of therapists and robotic systems. However, its broader adoption remains limited due to insufficient safe interaction and limited adaptation capability. This article proposes a novel telerobotics-mediated framework that enables therapists to intuitively and safely deliver assist-as-needed~(AAN) therapy based on two primary contributions. First, our framework encodes the therapist-informed corrective force into via-points in a latent space, allowing the therapist to provide only minimal assistance while encouraging patient maintaining own motion preferences. Second, a shape-adaptive ANN rehabilitation policy is learned to partially and progressively deform the reference trajectory for movement therapy based on encoded patient motion preferences and therapist-informed via-points. The effectiveness of the proposed shape-adaptive AAN strategy was validated on a telerobotic rehabilitation system using two representative tasks. The results demonstrate its practicality for remote AAN therapy and its superiority over two state-of-the-art methods in reducing corrective force and improving movement smoothness.

翻译：治疗师在环的机器人康复通过整合治疗师与机器人系统的优势，在提升康复效果方面展现出巨大潜力。然而，由于安全交互不足和适应能力有限，其广泛应用仍受到制约。本文提出一种新颖的遥操作机器人介导框架，基于两项核心贡献，使治疗师能够直观且安全地实施按需辅助（AAN）治疗。首先，我们的框架将治疗师指导的矫正力编码为潜在空间中的路径点，使治疗师仅需提供最小辅助，同时鼓励患者保持自身的运动偏好。其次，通过习得一种形状自适应的AAN康复策略，能够基于编码的患者运动偏好和治疗师指导的路径点，对运动治疗的参考轨迹进行局部渐进式形变。所提出的形状自适应AAN策略的有效性在一个遥操作康复系统上通过两项代表性任务得到验证。结果表明，该策略在远程AAN治疗中具有实用性，且在减少矫正力和提升运动平滑度方面优于两种先进方法。