arXiv 2510.046662026-02-22eess.SYcs.ROcs.SY

从治疗师指导的输入中学习形状自适应的按需康复策略

Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input

Division of Industrial Data Science (DIDS), School of Data Science, Lingnan University（工业数据科学系（DIDS），数据科学学院，岭南大学）；School of Mechanical Engineering, Tongji University（机械工程学院，同济大学）；Chair of Robotics and Systems Intelligence, MIRMI-Munich Institute of Robotics and Machine Intelligence, Technical University of Munich（机器人与系统智能 chair，慕尼黑工业大学机器人与机器智能研究所）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE（马尔代夫 bin Zayed 大学人工智能，阿布扎赫德，阿联酋）

Zhimin Hou, Jiacheng Hou, Xiao Chen, Hamid Sadeghian, Tianyu Ren, Sami Haddadin

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AI总结：

本文提出了一种基于治疗师指导的输入，通过形状自适应的ANN策略实现按需康复的远程机器人框架，有效减少纠正力并提升运动平滑度。

摘要：

治疗师参与的机器人康复已显示出通过结合治疗师和机器人系统的优点来增强康复结果的广阔前景。然而，其广泛应用仍受限制，因为安全交互不足且适应能力有限。本文提出了一种新颖的远程机器人介导框架，使治疗师能够根据两个主要贡献直观且安全地提供按需（AAN）疗法。首先，我们的框架将治疗师指导的纠正力编码为潜在空间中的路径点，使治疗师只需提供最小的协助，同时鼓励患者保持自己的运动偏好。第二，一种形状自适应的ANN康复策略被学习，以根据编码的患者运动偏好和治疗师指导的路径点，部分且逐步变形参考轨迹以进行运动疗法。所提出的形状自适应AAN策略的有效性在远程机器人康复系统上通过两个代表性任务进行了验证。结果证明了其在远程AAN疗法中的实用性，并在减少纠正力和提高运动平滑度方面优于两种最先进的方法。

英文摘要：

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.

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