Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym
Learning Skill Training Schedules From Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym
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DOI：
10.1109/tnsre.2023.3326777
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发表时间：
2023-01-01
影响因子：
4.9
通讯作者：
Jiang，Chao
中科院分区：
文献类型：
--
作者：
Adhikari，Bikranta;Bharadwaj，Varun R.;Jiang，Chao
具有多个康复机器人的机器人健身房允许多个患者在单个治疗师的监督下同时锻炼。通过基于监测的患者数据动态地将患者分配给机器人，可以潜在地改善多患者训练结果。在本文中，我们提出了一种方法来学习动态病人机器人分配从一个领域的专家通过监督学习。动态分配算法使用神经网络模型来预测患者之间的分配优先级。该神经网络使用在模拟康复健身房中创建的合成数据集进行训练，以模仿领域专家的分配行为。该方法进行了评估，在三个模拟场景具有不同的复杂性和不同的专家行为，以实现不同的培训目标。评估结果表明，我们的分配算法模仿专家的行为，平均准确率范围从75.4%到84.5%的情况下，显着优于三个基线分配方法的平均技能增益。我们的方法解决了简化的患者培训调度问题，而无需完全了解患者技能获取动态，并利用人类知识来学习自动分配策略。
A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to robots based on monitored patient data. In this paper, we present an approach to learn dynamic patient-robot assignment from a domain expert via supervised learning. The dynamic assignment algorithm uses a neural network model to predict assignment priorities between patients. This neural network was trained using a synthetic dataset created in a simulated rehabilitation gym to imitate a domain expert’s assignment behavior. The approach is evaluated in three simulated scenarios with different complexities and different expert behaviors meant to achieve different training objectives. Evaluation results show that our assignment algorithm imitates the expert’s behavior with mean accuracies ranging from 75.4% to 84.5% across scenarios and significantly outperforms three baseline assignment methods with respect to mean skill gain. Our approach solves simplified patient training scheduling problems without complete knowledge about the patient skill acquisition dynamics and leverages human knowledge to learn automated assignment policies.