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arXiv 2603.02458cs.RO
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从治疗师-外骨骼-患者互动中学习治疗师策略

Learning Therapist Policy from Therapist-Exoskeleton-Patient Interaction

Center for Robotics and Biosystems, Northwestern University（机器人与生物系统中心，西北大学）；Shirley Ryan AbilityLab（希拉里·瑞安能力实验室）

Grayson Snyder, Lorenzo Vianello, Levi Hargrove, Matthew L. Elwin, Jose Pons

备注：
Accepted at IEEE International Conference on Robotics and Automation (ICRA 2026)
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AI总结：

本文提出PTFF和ST模型，用于中风康复中通过机器人辅助的物理互动，以优化治疗师策略和康复效果。

摘要：

中风后康复通常需要患者恢复正常的行走步态。然而，传统的康复过程对治疗师来说可能是耗力且具有身体负担的，这可能会降低治疗的强度、持续时间和一致性。我们提出了一种患者-治疗师力场（PTFF）来可视化治疗师对患者运动学的响应，以及一个合成治疗师（ST）机器学习模型来支持治疗师在双人机器人介导的物理互动康复中。第一个将患者和治疗师的步态运动学编码到共享的低维潜在流形中，使用变分自编码器（VAE）并利用高斯混合模型（GMM）建模他们的互动，该模型学习了一个概率向量场映射，将患者潜在状态映射到治疗师的响应。这种表示方法可视化了患者-治疗师互动动态，以指导康复策略和机器人控制器设计。后者实现为一个长短期记忆（LSTM）网络，通过患者-治疗师互动数据训练，以预测治疗师施加的关节扭矩。通过留一法交叉验证在八名中风患者上训练和验证，模型被集成到基于ROS的外骨骼控制器中，以根据预测的治疗师响应生成实时扭矩辅助。离线结果和初步测试表明，其作为中风外骨骼康复替代方法的潜力。PTFF提供了对治疗师行为的理解，而ST使人类治疗师能够脱离外骨骼，使他们能够持续监控患者的细微状况。

英文摘要：

Post-stroke rehabilitation is often necessary for patients to regain proper walking gait. However, the typical therapy process can be exhausting and physically demanding for therapists, potentially reducing therapy intensity, duration, and consistency over time. We propose a Patient-Therapist Force Field (PTFF) to visualize therapist responses to patient kinematics and a Synthetic Therapist (ST) machine learning model to support the therapist in dyadic robot-mediated physical interaction therapy. The first encodes patient and therapist stride kinematics into a shared low-dimensional latent manifold using a Variational Autoencoder (VAE) and models their interaction through a Gaussian Mixture Model (GMM), which learns a probabilistic vector field mapping patient latent states to therapist responses. This representation visualizes patient-therapist interaction dynamics to inform therapy strategies and robot controller design. The latter is implemented as a Long Short-Term Memory (LSTM) network trained on patient-therapist interaction data to predict therapist-applied joint torques from patient kinematics. Trained and validated using leave-one-out cross-validation across eight post-stroke patients, the model was integrated into a ROS-based exoskeleton controller to generate real-time torque assistance based on predicted therapist responses. Offline results and preliminary testing indicate the potential of their use as an alternative approach to post-stroke exoskeleton therapy. The PTFF provides understanding of the therapist's actions while the ST frees the human therapist from the exoskeleton, allowing them to continuously monitor the patient's nuanced condition.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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