Reinforcement learning-based target grasping for low cost robotic arm

Reinforcement learning-based target grasping for low cost robotic arm

This dissertation focuses on grasping targets by robotic arms based on deep reinforcement learning. Theoretically, it goes into the application of the most advanced algorithms of reinforcement learning, particularly the Soft Actor-Critic algorithm. It will choose Stable Baselines3 for many advant...

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Main Author: Yuan, Weibin
Other Authors: Wen Bihan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Engineering
Online Access:https://hdl.handle.net/10356/180764
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1807642024-10-25T15:45:38Z Reinforcement learning-based target grasping for low cost robotic arm Yuan, Weibin Wen Bihan School of Electrical and Electronic Engineering bihan.wen@ntu.edu.sg Computer and Information Science Engineering This dissertation focuses on grasping targets by robotic arms based on deep reinforcement learning. Theoretically, it goes into the application of the most advanced algorithms of reinforcement learning, particularly the Soft Actor-Critic algorithm. It will choose Stable Baselines3 for many advantages: verified set of algorithms, compatibility with PyTorch, and community support. The study has also been conducted with detailed analysis in terms of the structure of the robotic arm through juxtaposition of traditional control methods against reinforcement learning algorithms to gain a deeper understanding as to the various ways of controlling a robotic arm. Master's degree 2024-10-23T04:22:29Z 2024-10-23T04:22:29Z 2024 Thesis-Master by Coursework Yuan, W. (2024). Reinforcement learning-based target grasping for low cost robotic arm. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180764 https://hdl.handle.net/10356/180764 en 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 Computer and Information Science
Engineering
spellingShingle Computer and Information Science
Engineering
Yuan, Weibin
Reinforcement learning-based target grasping for low cost robotic arm
description This dissertation focuses on grasping targets by robotic arms based on deep reinforcement learning. Theoretically, it goes into the application of the most advanced algorithms of reinforcement learning, particularly the Soft Actor-Critic algorithm. It will choose Stable Baselines3 for many advantages: verified set of algorithms, compatibility with PyTorch, and community support. The study has also been conducted with detailed analysis in terms of the structure of the robotic arm through juxtaposition of traditional control methods against reinforcement learning algorithms to gain a deeper understanding as to the various ways of controlling a robotic arm.
author2 Wen Bihan
author_facet Wen Bihan
Yuan, Weibin
format Thesis-Master by Coursework
author Yuan, Weibin
author_sort Yuan, Weibin
title Reinforcement learning-based target grasping for low cost robotic arm
title_short Reinforcement learning-based target grasping for low cost robotic arm
title_full Reinforcement learning-based target grasping for low cost robotic arm
title_fullStr Reinforcement learning-based target grasping for low cost robotic arm
title_full_unstemmed Reinforcement learning-based target grasping for low cost robotic arm
title_sort reinforcement learning-based target grasping for low cost robotic arm
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/180764
_version_ 1814777794373091328

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