Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908845233668096 |
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| author | Hou, Zhimin Hou, Jiacheng Chen, Xiao Sadeghian, Hamid Ren, Tianyu Haddadin, Sami |
| author_facet | Hou, Zhimin Hou, Jiacheng Chen, Xiao Sadeghian, Hamid Ren, Tianyu Haddadin, Sami |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04666 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input Hou, Zhimin Hou, Jiacheng Chen, Xiao Sadeghian, Hamid Ren, Tianyu Haddadin, Sami Systems and Control Robotics 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. |
| title | Learning a Shape-adaptive Assist-as-needed Rehabilitation Policy from Therapist-informed Input |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2510.04666 |