Learning User-Specific Control Policies for Lower-Limb Exoskeletons Using Gaussian Process Regression

Abstract

Robotic exoskeletons provide a viable means for enabling individuals with limited or no walking ability to traverse various surfaces with maximal external support to the patient’s body. However, to achieve effective performance, it is crucial to consider anatomical differences in body size and shape among users. In this paper, we propose a framework to infer adapted user-specific policies using a small dataset from past experiments performed with twelve users wearing a lower-limb self-balancing exoskeleton. Our framework utilizes Gaussian Process Regression (GPR) to learn a mapping between user characteristics and control policy parameters. We also propose to use hindsight data relabeling to improve the performance of the controller. We experimentally test the output of the GPR model on new users and demonstrate its effectiveness in predicting user-specific walking parameters that lead to high performance. We also compare the performance of this control policy with an expert-tuned policy and show that our framework can reach comparable results without the need to perform expensive and unsafe tuning of the controller for new users.

Keywords

• Exoskeletons and prosthetics
• machine learning for robot control
• bipedal locomotion