发表机构
The University of Melbourne（墨尔本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对康复机器人在任务特定训练中未体现显著优势的问题，提出基于TPGMM的演示学习框架，学习个性化治疗师-患者交互，在多任务评估中其表现略优于查找表。
AI 中文摘要
上肢运动功能恢复与任务特定训练（TST）及充足的治疗剂量呈正相关。康复机器人可通过受控的重复治疗增加TST剂量，并解放治疗师同时管理其他患者，但尚未证明其相比传统治疗有显著优势，这可能与机器人对个性化治疗师-患者交互的不准确表征，以及TST过程中练习变异性不足有关。因此，我们倡导在为处于不同练习条件的患者提供TST时，保留个性化治疗师-患者交互的机器人干预方案。我们提出一种基于演示学习的框架，采用任务参数化高斯混合模型（TPGMM）学习任务特定练习中的个性化治疗师-患者交互，利用少量演示将患者关节运动学映射到治疗师施加的扭矩。该模型可泛化到新任务变体中重建治疗师扭矩。我们对14对模拟“治疗师-患者”在三个复杂度递增的任务（每个任务含6种变体）中的物理交互进行了评估，并与查找表（Look-Up Table）开展基准比较。结果显示，两种方法均能在与实际交互略有偏差的未见过的任务变体中复现交互，其中TPGMM的表现略优于查找表；两种方法复现的交互均随任务复杂度提升而越来越接近实际交互。
英文摘要
Upper extremity motor function recovery is positively linked to Task-Specific Training (TST) and sufficient therapy dosage. Rehabilitation robots can increase TST dosage via controlled, repetitive treatment and free therapists to simultaneously manage other patients, but it has yet to demonstrate significant benefits over conventional treatment. This is potentially linked to inaccurate robotic representation of personalised physical therapist-patient interaction and lack of practice variability during TST. Hence, we advocate for robotic interventions that preserve the personalised physical therapist-patient interactions when delivering TST for patients across varying practise conditions. We propose a Learning-from-Demonstration framework using Task-Parameterised Gaussian Mixture Models (TPGMM) to learn personalised physical therapist-patient interaction in Task-Specific exercises, mapping patient joint kinematics to therapist-applied torques using few demonstrations. The model is generalised to reconstruct therapist torques in new task variations. The framework was evaluated on physical interactions from 14 mock "therapist-patient" pairs over three tasks of increasing complexity, each with six variations. A benchmark comparison against a Look-Up Table was conducted. The results show both methods reproducing interactions in unseen task variations that deviate slightly from the actual interaction, with TPGMM slightly outperforming LUT. Both methods reproduced interactions that gets increasingly closer to the actual interaction as task complexity increases.
Comments12 pages, 4 figures, 3 tables Submitted to:IEEE Transactions on Neural Systems and Rehabilitation Engineering
