# Using Human Ratings for Feedback Control: A Supervised Learning Approach with Application to Rehabilitation Robotics

## TL;DR

This paper introduces a supervised learning-based method to optimize a rehabilitation robot’s control system using human ratings, enabling physiologically accurate gait patterns with minimal adjustments.

## Contribution

It presents a novel approach that uses human ratings to train a reward model for controlling a gait rehabilitation robot, improving physiological walking outcomes.

## Key findings

- The method successfully teaches the robot to walk physiologically.
- Few input adaptations are needed to achieve desired gait patterns.
- Experiments validate the incorporation of human expertise into control law.

## Abstract

This paper presents a method for tailoring a parametric controller based on human ratings. The method leverages supervised learning concepts in order to train a reward model from data. It is applied to a gait rehabilitation robot with the goal of teaching the robot how to walk patients physiologically. In this context, the reward model judges the physiology of the gait cycle (instead of therapists) using sensor measurements provided by the robot and the automatic feedback controller chooses the input settings of the robot to maximize the reward. The key advantage of the proposed method is that only a few input adaptations are necessary to achieve a physiological gait cycle. Experiments with nondisabled subjects show that the proposed method permits the incorporation of human expertise into a control law and to automatically walk patients physiologically.