The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works. However, their practical utility is dependent on the deployment of appropriate control algorithms, which adapt the level of task-assistance according to each individual patient's need. Generally, the required personalization is achieved through manual tuning by clinicians, which is cumbersome and error-prone. In this work we propose a novel online learning control architecture, which is able to personalize the control force at run time to each individual user. To this end, we deploy Gaussian process-based online learning with previously unseen prediction and update rates. Finally, we evaluate our method in an experimental user study, where the learning controller is shown to provide personalized control, while also obtaining safe interaction forces.
翻译:临床应用中康复机器人的使用由于治疗的好处和减轻劳动密集型工程的能力而越来越重要。然而,这些机器人的实际用途取决于是否部署适当的控制算法,这种算法根据每个病人的需要调整任务援助的水平。一般而言,所需的个性化是通过临床医生的人工调整实现的,因为临床应用是繁琐和容易出错的。在这项工作中,我们建议建立一个新型的在线学习控制结构,它能够使每个使用者在运行时掌握控制力量的个人化。为此,我们部署基于Gaussian程序的在线学习,并采用先前的不为人知的预测和更新速度。最后,我们在一项实验用户研究中评估我们的方法,在实验用户研究中显示学习控制者提供个性化控制,同时获得安全的互动力量。