Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

Linda  van der Spaa; Michael Gienger; Tamas Bates; Jens Kober

doi:10.1109/ICRA40945.2020.9197296

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

Linda van der Spaa, Michael Gienger, Tamas Bates, Jens Kober

Learning & Autonomous Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

36 Citations (Scopus)

67 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2020
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	1799-1805
ISBN (Electronic)	978-1-7281-7395-5
DOIs	https://doi.org/10.1109/ICRA40945.2020.9197296
Publication status	Published - 2020
Event	2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France Duration: 31 May 2020 → 31 Aug 2020

Conference

Conference	2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/Territory	France
City	Paris
Period	31/05/20 → 31/08/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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title = "Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks",

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.",

author = "{van der Spaa}, Linda and Michael Gienger and Tamas Bates and Jens Kober",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 ; Conference date: 31-05-2020 Through 31-08-2020",

year = "2020",

doi = "10.1109/ICRA40945.2020.9197296",

language = "English",

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booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2020",

publisher = "IEEE",

address = "United States",

}

van der Spaa, L, Gienger, M, Bates, T & Kober, J 2020, Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks. in Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2020. IEEE, Piscataway, NJ, USA, pp. 1799-1805, 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, 31/05/20. https://doi.org/10.1109/ICRA40945.2020.9197296

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks. / van der Spaa, Linda ; Gienger, Michael; Bates, Tamas et al.
Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2020. Piscataway, NJ, USA: IEEE, 2020. p. 1799-1805.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Bates, Tamas

AU - Kober, Jens

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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N2 - 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.

AB - 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.

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ER -

Predicting and Optimizing Ergonomics in Physical Human-Robot Cooperation Tasks

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