Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the...

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Autores principales:	Iñigo Iturrate, Aljaz Kramberger, Christoffer Sloth
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	learning from demonstration parameter estimation force control gluing adaptive control Mechanical engineering and machinery TJ1-1570 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/5d65fa20ff3e493896478a4c93814e37
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spelling	oai:doaj.org-article:5d65fa20ff3e493896478a4c93814e372021-11-05T10:37:02ZQuick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration2296-914410.3389/frobt.2021.767878https://doaj.org/article/5d65fa20ff3e493896478a4c93814e372021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frobt.2021.767878/fullhttps://doaj.org/toc/2296-9144This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry.Iñigo IturrateAljaz KrambergerChristoffer SlothFrontiers Media S.A.articlelearning from demonstrationparameter estimationforce controlgluingadaptive controlMechanical engineering and machineryTJ1-1570Electronic computers. Computer scienceQA75.5-76.95ENFrontiers in Robotics and AI, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	learning from demonstration parameter estimation force control gluing adaptive control Mechanical engineering and machinery TJ1-1570 Electronic computers. Computer science QA75.5-76.95
spellingShingle	learning from demonstration parameter estimation force control gluing adaptive control Mechanical engineering and machinery TJ1-1570 Electronic computers. Computer science QA75.5-76.95 Iñigo Iturrate Aljaz Kramberger Christoffer Sloth Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
description	This paper presents a framework for programming in-contact tasks using learning by demonstration. The framework is demonstrated on an industrial gluing task, showing that a high quality robot behavior can be programmed using a single demonstration. A unified controller structure is proposed for the demonstration and execution of in-contact tasks that eases the transition from admittance controller for demonstration to parallel force/position control for the execution. The proposed controller is adapted according to the geometry of the task constraints, which is estimated online during the demonstration. In addition, the controller gains are adapted to the human behavior during demonstration to improve the quality of the demonstration. The considered gluing task requires the robot to alternate between free motion and in-contact motion; hence, an approach for minimizing contact forces during the switching between the two situations is presented. We evaluate our proposed system in a series of experiments, where we show that we are able to estimate the geometry of a curved surface, that our adaptive controller for demonstration allows users to achieve higher accuracy in a shorter demonstration duration when compared to an off-the-shelf controller for teaching implemented on a collaborative robot, and that our execution controller is able to reduce impact forces and apply a constant process force while adapting to the surface geometry.
format	article
author	Iñigo Iturrate Aljaz Kramberger Christoffer Sloth
author_facet	Iñigo Iturrate Aljaz Kramberger Christoffer Sloth
author_sort	Iñigo Iturrate
title	Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
title_short	Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
title_full	Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
title_fullStr	Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
title_full_unstemmed	Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration
title_sort	quick setup of force-controlled industrial gluing tasks using learning from demonstration
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/5d65fa20ff3e493896478a4c93814e37
work_keys_str_mv	AT inigoiturrate quicksetupofforcecontrolledindustrialgluingtasksusinglearningfromdemonstration AT aljazkramberger quicksetupofforcecontrolledindustrialgluingtasksusinglearningfromdemonstration AT christoffersloth quicksetupofforcecontrolledindustrialgluingtasksusinglearningfromdemonstration
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Quick Setup of Force-Controlled Industrial Gluing Tasks Using Learning From Demonstration

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