Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

Conference paper (2020)

Authors

L.F. van der Spaa Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Michael Gienger Honda Research Institute Europe
T. Bates Honda Research Institute Europe, Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 L.F. van der Spaa, Michael Gienger, T. Bates, J. Kober
DOI
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https://doi.org/10.1109/ICRA40945.2020.9197296
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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.