Learning Dynamic Patient-Robot Task Assignment and Scheduling for A Robotic Rehabilitation Gym
Learning Dynamic Patient-Robot Task Assignment and Scheduling for A Robotic Rehabilitation Gym
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DOI：
10.1109/icorr55369.2022.9896498
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Bikranta Adhikari;Shivanjali Ranashing;Benjamin A. Miller;Vesna D. Novak;Chao Jiang
中科院分区：
文献类型：
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作者：
Bikranta Adhikari;Shivanjali Ranashing;Benjamin A. Miller;Vesna D. Novak;Chao Jiang
机器人康复健身房是一种允许多名患者使用多个机器人一起锻炼的设置。在这样的群体环境中训练的有效性可以通过动态地将患者分配给特定的机器人来提高。在这项模拟研究中，我们开发了一个自动化系统，动态地使病人机器人分配的基础上测得的病人的表现，以实现最佳的群体康复结果。为了解决动态分配问题，我们提出了一种方法，使用神经网络分类器来预测两个病人之间的分配优先级为一个特定的机器人给定他们的任务成功率的机器人。使用领域专家提供的分配演示来训练优先级分类器。在没有来自机器人健身房的真实的人类数据的情况下，我们开发了一个机器人健身房模拟器，并创建了一个用于训练分类器的合成数据集。模拟结果表明，我们的方法，使有效的分配，产生可比的患者培训结果的领域专家。
A robotic rehabilitation gym is a setup that allows multiple patients to exercise together using multiple robots. The effectiveness of training in such a group setting could be increased by dynamically assigning patients to specific robots. In this simulation study, we develop an automated system that dynamically makes patient-robot assignments based on measured patient performance to achieve optimal group rehabilitation outcome. To solve the dynamic assignment problem, we propose an approach that uses a neural network classifier to predict the assignment priority between two patients for a specific robot given their task success rate on that robot. The priority classifier is trained using assignment demonstrations provided by a domain expert. In the absence of real human data from a robotic gym, we develop a robotic gym simulator and create a synthetic dataset for training the classifier. The simulation results show that our approach makes effective assignments that yield comparable patient training outcomes to those obtained by the domain expert.