Curriculum learning for robotic peg-in-hole assembly

Robotic assembly is very essential in the smart industry. With the help of Deep Learning technology, the robots gain the ability to perform more complex tasks with less human involvement, and become more adaptive to the environmental changes, compared to the conventional ways of fine tuning the robo...

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Main Author: He, Zhanxin
Other Authors: Pham Quang Cuong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/150460
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1504602021-05-28T06:58:26Z Curriculum learning for robotic peg-in-hole assembly He, Zhanxin Pham Quang Cuong School of Mechanical and Aerospace Engineering Robotics Research Centre cuong@ntu.edu.sg Engineering::Mechanical engineering Robotic assembly is very essential in the smart industry. With the help of Deep Learning technology, the robots gain the ability to perform more complex tasks with less human involvement, and become more adaptive to the environmental changes, compared to the conventional ways of fine tuning the robot strategies and parameters. One of the most interesting and trending tasks is the peg-in-hole insertion tasks where the robot needs to insert a peg or pin into a hole. A lot of research has been done to increase the performance for high precision insertion tasks and deep reinforcement learning is a convincing method to realize the purpose. In this project, a curriculum learning methodology is proposed and applied to train the robot from easier tasks, such as larger clearances, and transfer the learned knowledge to train with more difficult tasks. The training algorithm in each curriculum followed is a deep reinforcement learning method. The results have shown the potential that the curriculum learning is capable for training the robot to perform much more difficult tasks that are failed by the direct training. Bachelor of Engineering (Mechanical Engineering) 2021-05-28T06:58:26Z 2021-05-28T06:58:26Z 2021 Final Year Project (FYP) He, Z. (2021). Curriculum learning for robotic peg-in-hole assembly. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150460 https://hdl.handle.net/10356/150460 en C063 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
He, Zhanxin
Curriculum learning for robotic peg-in-hole assembly
description Robotic assembly is very essential in the smart industry. With the help of Deep Learning technology, the robots gain the ability to perform more complex tasks with less human involvement, and become more adaptive to the environmental changes, compared to the conventional ways of fine tuning the robot strategies and parameters. One of the most interesting and trending tasks is the peg-in-hole insertion tasks where the robot needs to insert a peg or pin into a hole. A lot of research has been done to increase the performance for high precision insertion tasks and deep reinforcement learning is a convincing method to realize the purpose. In this project, a curriculum learning methodology is proposed and applied to train the robot from easier tasks, such as larger clearances, and transfer the learned knowledge to train with more difficult tasks. The training algorithm in each curriculum followed is a deep reinforcement learning method. The results have shown the potential that the curriculum learning is capable for training the robot to perform much more difficult tasks that are failed by the direct training.
author2 Pham Quang Cuong
author_facet Pham Quang Cuong
He, Zhanxin
format Final Year Project
author He, Zhanxin
author_sort He, Zhanxin
title Curriculum learning for robotic peg-in-hole assembly
title_short Curriculum learning for robotic peg-in-hole assembly
title_full Curriculum learning for robotic peg-in-hole assembly
title_fullStr Curriculum learning for robotic peg-in-hole assembly
title_full_unstemmed Curriculum learning for robotic peg-in-hole assembly
title_sort curriculum learning for robotic peg-in-hole assembly
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150460
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