Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks
Linda van der Spaa, Michael Gienger, Tamas Bates, Jens Kober
Abstract
This paper presents a method to incorporate ergonomics into the optimization of action sequences for bi-manual human-robot cooperation tasks with continuous physical interaction. Our first contribution is a novel computational model of the human that allows prediction of an ergonomics assessment corresponding to each step in a task. The model is learned from human motion capture data in order to predict the human pose as realistically as possible. The second contribution is a combination of this prediction model with an informed graph search algorithm, which allows computation of human-robot cooperative plans with improved ergonomics according to the incorporated method for ergonomic assessment. The concepts have been evaluated in simulation and in a small user study in which the subjects manipulate a large object with a 32 DoF bimanual mobile robot as partner. For all subjects, the ergonomic-enhanced planner shows their reduced ergonomic cost compared to a baseline planner.
BibTeX
@inproceedings{icra2020_predictingandopt,
title = {Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks},
author = {Linda van der Spaa and Michael Gienger and Tamas Bates and Jens Kober},
booktitle = {ICRA 2020},
year = {2020}
}