Design of Fuzzy Logic Parameter Tuners for Upper-Limb Assistive Robots

TL;DR

This paper presents fuzzy logic controllers that dynamically tune hyper-parameters of assistive exoskeleton robots, improving user comfort and reducing device-human conflict without extensive training data.

Contribution

Introduction of fuzzy logic-based hyper-parameter tuners for assistive robots that adapt to user intentions using expert rules, eliminating the need for large training datasets.

Findings

Controllers reduce device-human conflict as measured by pressure sensors.

Adaptive tuning improves naturalness of robot assistance.

Method performs well across multiple tasks.

Abstract

Assistive Exoskeleton Robots are helping restore functions to people suffering from underlying medical conditions. These robots require precise tuning of hyper-parameters to feel natural to the user. The device hyper-parameters often need to be re-tuned from task to task, which can be tedious and require expert knowledge. To address this issue, we develop a set of fuzzy logic controllers that can dynamically tune robot gain parameters to adapt its sensitivity to the user's intention determined from muscle activation. The designed fuzzy controllers benefit from a set of expert-defined rules and do not rely on extensive amounts of training data. We evaluate the designed controllers with three different tasks and compare our results against the manually tuned system. Our preliminary results show that our controllers reduce the amount of fighting between the device and the human, measured using a set of pressure sensors.