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Improving human robot collaboration through force/torque based learning for object manipulation

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journal contribution
posted on 11.01.2021, 14:11 by Ali Al-Yacoub, Yuchen Zhao, Will Eaton, Yee Goh, Niels Lohse
Human-Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.

Funding

Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Robotics and Computer Integrated Manufacturing

Volume

69

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

06/12/2020

Publication date

2021-01-04

Copyright date

2021

ISSN

0736-5845

Language

en

Depositor

Dr Ali Al-Yacoub. Deposit date: 6 December 2020

Article number

102111

Licence

Exports