A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties
The thesis proposed a framework to train deep-reinforcement-learning agents for fine manipulation tasks with goal uncertainties. It consisted of three aspects: state-space formulation, artificial training-goals uncertainties, and progressive training. The framework was used in a simulation for two f...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2020
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| Online Access: | http://hdl.handle.net/20.500.11937/85887 |
| _version_ | 1848764776110358528 |
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| author | Rouillard, Thibault |
| author_facet | Rouillard, Thibault |
| author_sort | Rouillard, Thibault |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The thesis proposed a framework to train deep-reinforcement-learning agents for fine manipulation tasks with goal uncertainties. It consisted of three aspects: state-space formulation, artificial training-goals uncertainties, and progressive training. The framework was used in a simulation for two fine manipulation tasks, square Peg-in-Hole and round Peg-in-Hole. The resulting behaviours were then transferred a physical robotic manipulator and compared to traditional training methods. The deep-reinforcement-learning agents trained using the framework in this work outperformed those trained with definite goals. |
| first_indexed | 2025-11-14T11:24:43Z |
| format | Thesis |
| id | curtin-20.500.11937-85887 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:24:43Z |
| publishDate | 2020 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-858872021-10-05T05:39:11Z A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties Rouillard, Thibault The thesis proposed a framework to train deep-reinforcement-learning agents for fine manipulation tasks with goal uncertainties. It consisted of three aspects: state-space formulation, artificial training-goals uncertainties, and progressive training. The framework was used in a simulation for two fine manipulation tasks, square Peg-in-Hole and round Peg-in-Hole. The resulting behaviours were then transferred a physical robotic manipulator and compared to traditional training methods. The deep-reinforcement-learning agents trained using the framework in this work outperformed those trained with definite goals. 2020 Thesis http://hdl.handle.net/20.500.11937/85887 Curtin University fulltext |
| spellingShingle | Rouillard, Thibault A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties |
| title | A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties |
| title_full | A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties |
| title_fullStr | A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties |
| title_full_unstemmed | A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties |
| title_short | A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties |
| title_sort | deep-reinforcement-learning approach to the
peg-in-hole task with goal uncertainties |
| url | http://hdl.handle.net/20.500.11937/85887 |