Sample-Efficient Policy Adaptation for Exoskeletons Under Variations in the Users and the Environment

Controlling lower-limb exoskeletons is extremely challenging due to their direct physical interaction with users wearing them which imposes additional safety concerns. Furthermore, the control policy needs to adapt for different users and surfaces the robot is traversing. Hence, it is crucial to des...

Full description

Saved in:
Bibliographic Details
Published in:IEEE robotics and automation letters 2022-10, Vol.7 (4), p.9020-9027
Main Authors: Shahrokhshahi, Ahmadreza, Khadiv, Majid, Taherifar, Ali, Mansouri, Saeed, Park, Edward J., Arzanpour, Siamak
Format: Article
Language:English
Subjects:
Controllers
Dynamics
Exoskeletons
Humanoid and bipedal locomotion
Kinematics
Optimization
prosthetics and exoskeletons
Robots
Safety
Task analysis
Trajectory
Uncertainty
wearable robotics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Controlling lower-limb exoskeletons is extremely challenging due to their direct physical interaction with users wearing them which imposes additional safety concerns. Furthermore, the control policy needs to adapt for different users and surfaces the robot is traversing. Hence, it is crucial to design a control framework that can perform robustly in the presence of these variations. In this letter, we propose a sample-efficient method based on Bayesian Optimization (BO) to adapt a model-based walking controller for a lower-limb exoskeleton, XoMotion. In order to mitigate safety risks, we use a set of dummy weights with realistic inertial distributions in the experiments with the robot to find optimal policies. An extensive set of experimental results shows that the proposed controller can successfully adapt for different users and different terrains, in less than 15 real-world trials.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3187262