A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin...
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| Published in: | Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3762 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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05.04.2023
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| ISSN: | 1424-8220, 1424-8220 |
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| Abstract | Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. |
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| AbstractList | Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor-critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor-critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject. |
| Audience | Academic |
| Author | Stankovic, Vladimir Han, Dong Cheng, Samuel Mulyana, Beni |
| AuthorAffiliation | 1 School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA 2 Department of Electronic and Electrical Engineering, University of Straclyde, Glasglow G1 1XW, UK |
| AuthorAffiliation_xml | – name: 2 Department of Electronic and Electrical Engineering, University of Straclyde, Glasglow G1 1XW, UK – name: 1 School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA |
| Author_xml | – sequence: 1 givenname: Dong surname: Han fullname: Han, Dong – sequence: 2 givenname: Beni surname: Mulyana fullname: Mulyana, Beni – sequence: 3 givenname: Vladimir orcidid: 0000-0002-1075-2420 surname: Stankovic fullname: Stankovic, Vladimir – sequence: 4 givenname: Samuel orcidid: 0000-0002-5439-1137 surname: Cheng fullname: Cheng, Samuel |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37050822$$D View this record in MEDLINE/PubMed |
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