Learning Mobile Manipulation through Deep Reinforcement Learning
Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/939 |
| _version_ | 1811184057023725568 |
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| author | Cong Wang Qifeng Zhang Qiyan Tian Shuo Li Xiaohui Wang David Lane Yvan Petillot Sen Wang |
| author_facet | Cong Wang Qifeng Zhang Qiyan Tian Shuo Li Xiaohui Wang David Lane Yvan Petillot Sen Wang |
| author_sort | Cong Wang |
| collection | DOAJ |
| description | Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system. |
| first_indexed | 2024-04-11T13:06:50Z |
| format | Article |
| id | doaj.art-32ba2c08e4364b51864f43d6682c073c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:06:50Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-32ba2c08e4364b51864f43d6682c073c2022-12-22T04:22:41ZengMDPI AGSensors1424-82202020-02-0120393910.3390/s20030939s20030939Learning Mobile Manipulation through Deep Reinforcement LearningCong Wang0Qifeng Zhang1Qiyan Tian2Shuo Li3Xiaohui Wang4David Lane5Yvan Petillot6Sen Wang7State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaSchool of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UKSchool of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UKSchool of Engineering & Physical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UKMobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.https://www.mdpi.com/1424-8220/20/3/939mobile manipulationdeep reinforcement learningdeep learning |
| spellingShingle | Cong Wang Qifeng Zhang Qiyan Tian Shuo Li Xiaohui Wang David Lane Yvan Petillot Sen Wang Learning Mobile Manipulation through Deep Reinforcement Learning Sensors mobile manipulation deep reinforcement learning deep learning |
| title | Learning Mobile Manipulation through Deep Reinforcement Learning |
| title_full | Learning Mobile Manipulation through Deep Reinforcement Learning |
| title_fullStr | Learning Mobile Manipulation through Deep Reinforcement Learning |
| title_full_unstemmed | Learning Mobile Manipulation through Deep Reinforcement Learning |
| title_short | Learning Mobile Manipulation through Deep Reinforcement Learning |
| title_sort | learning mobile manipulation through deep reinforcement learning |
| topic | mobile manipulation deep reinforcement learning deep learning |
| url | https://www.mdpi.com/1424-8220/20/3/939 |
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