Using Human Ratings for Feedback Control: A Supervised Learning Approach with Application to Rehabilitation Robotics
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Date
2020-06
Publication Type
Journal Article
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yes
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Abstract
This article 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. (© IEEE 2004-2012)
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published
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Journal / series
Volume
36 (3)
Pages / Article No.
789 - 801
Publisher
IEEE
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Software
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Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
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Funding
157601 - Safety and Performance for Human in the Loop Control (SNF)
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Is cited by: https://doi.org/10.3929/ethz-b-000449554