Learning compliant box-in-box insertion through haptic-based robotic teleoperation

In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are...

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Main Authors: Kana, Sreekanth, Gurnani, Juhi, Ramanathan, Vishal, Ariffin, Mohammad Zaidi, Turlapati, Sri Harsha, Campolo, Domenico
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173990
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1739902024-03-16T16:48:24Z Learning compliant box-in-box insertion through haptic-based robotic teleoperation Kana, Sreekanth Gurnani, Juhi Ramanathan, Vishal Ariffin, Mohammad Zaidi Turlapati, Sri Harsha Campolo, Domenico School of Mechanical and Aerospace Engineering Engineering Box-in-box insertion Compliant insertion In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques. The proposed framework has three phases. Firstly, a master-slave teleoperated robot system is used in the initial phase to haptically demonstrate the insertion task. Then, the learning phase involves identifying trends in the demonstrated trajectories using probabilistic methods, in this case, Gaussian Mixture Regression. In the third phase, the insertion task is generalised, and the robot adjusts to any object position using barycentric interpolation. This method is novel because it tackles tight insertion by taking advantage of the boxes' natural compliance, making it possible to complete the task even with a position-controlled robot. To determine whether the strategy is generalisable and repeatable, experimental validation was carried out. Agency for Science, Technology and Research (A*STAR) Published version This research was conducted under project WP3 within the Delta-NTU Corporate Lab with funding support from A*STAR under its IAF-ICP programme (Grant no: I2201E0013) and Delta Electronics Inc. 2024-03-11T00:40:05Z 2024-03-11T00:40:05Z 2023 Journal Article Kana, S., Gurnani, J., Ramanathan, V., Ariffin, M. Z., Turlapati, S. H. & Campolo, D. (2023). Learning compliant box-in-box insertion through haptic-based robotic teleoperation. Sensors, 23(21), 8721-. https://dx.doi.org/10.3390/s23218721 1424-8220 https://hdl.handle.net/10356/173990 10.3390/s23218721 37960421 2-s2.0-85176890256 21 23 8721 en I2201E0013 Sensors © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Box-in-box insertion
Compliant insertion
spellingShingle Engineering
Box-in-box insertion
Compliant insertion
Kana, Sreekanth
Gurnani, Juhi
Ramanathan, Vishal
Ariffin, Mohammad Zaidi
Turlapati, Sri Harsha
Campolo, Domenico
Learning compliant box-in-box insertion through haptic-based robotic teleoperation
description In modern logistics, the box-in-box insertion task is representative of a wide range of packaging applications, and automating compliant object insertion is difficult due to challenges in modelling the object deformation during insertion. Using Learning from Demonstration (LfD) paradigms, which are frequently used in robotics to facilitate skill transfer from humans to robots, can be one solution for complex tasks that are difficult to mathematically model. In order to automate the box-in-box insertion task for packaging applications, this study makes use of LfD techniques. The proposed framework has three phases. Firstly, a master-slave teleoperated robot system is used in the initial phase to haptically demonstrate the insertion task. Then, the learning phase involves identifying trends in the demonstrated trajectories using probabilistic methods, in this case, Gaussian Mixture Regression. In the third phase, the insertion task is generalised, and the robot adjusts to any object position using barycentric interpolation. This method is novel because it tackles tight insertion by taking advantage of the boxes' natural compliance, making it possible to complete the task even with a position-controlled robot. To determine whether the strategy is generalisable and repeatable, experimental validation was carried out.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Kana, Sreekanth
Gurnani, Juhi
Ramanathan, Vishal
Ariffin, Mohammad Zaidi
Turlapati, Sri Harsha
Campolo, Domenico
format Article
author Kana, Sreekanth
Gurnani, Juhi
Ramanathan, Vishal
Ariffin, Mohammad Zaidi
Turlapati, Sri Harsha
Campolo, Domenico
author_sort Kana, Sreekanth
title Learning compliant box-in-box insertion through haptic-based robotic teleoperation
title_short Learning compliant box-in-box insertion through haptic-based robotic teleoperation
title_full Learning compliant box-in-box insertion through haptic-based robotic teleoperation
title_fullStr Learning compliant box-in-box insertion through haptic-based robotic teleoperation
title_full_unstemmed Learning compliant box-in-box insertion through haptic-based robotic teleoperation
title_sort learning compliant box-in-box insertion through haptic-based robotic teleoperation
publishDate 2024
url https://hdl.handle.net/10356/173990
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