Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence
This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10493015/ |
| _version_ | 1797206109009739776 |
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| author | Recep Ozalp Aysegul Ucar Cuneyt Guzelis |
| author_facet | Recep Ozalp Aysegul Ucar Cuneyt Guzelis |
| author_sort | Recep Ozalp |
| collection | DOAJ |
| description | This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipulation with uncertain rewards, multitasking, transfer learning, multimodal, and Human-Robot Interaction (HRI). The articles are summarized in terms of the main contributions, methods, challenges, and highlights of the latest and relevant studies using DRL and IRL for robotic manipulation. Additionally, summary tables regarding the problem and solution are presented. The literature review then focuses on the concepts of trustworthy AI, interpretable AI, and explainable AI (XAI) in the context of robotic manipulation. Moreover, this review provides a resource for future research on DRL/IRL in trustworthy robotic manipulation. |
| first_indexed | 2024-04-24T09:01:47Z |
| format | Article |
| id | doaj.art-634670762b3945d58be0b2300d0d87ca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T09:01:47Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-634670762b3945d58be0b2300d0d87ca2024-04-15T23:00:26ZengIEEEIEEE Access2169-35362024-01-0112518405185810.1109/ACCESS.2024.338542610493015Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial IntelligenceRecep Ozalp0https://orcid.org/0000-0001-6343-0372Aysegul Ucar1https://orcid.org/0000-0002-5253-3779Cuneyt Guzelis2https://orcid.org/0000-0001-5416-368XMechatronics Engineering Department, Engineering Faculty, Firat University, Elâziğ, TurkeyMechatronics Engineering Department, Engineering Faculty, Firat University, Elâziğ, TurkeyEngineering Faculty, Electrical and Electronics Engineering, Yaşar University, İzmir, TurkeyThis article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks. The reviewed articles are examined in various categories, including DRL and IRL for perception, assembly, manipulation with uncertain rewards, multitasking, transfer learning, multimodal, and Human-Robot Interaction (HRI). The articles are summarized in terms of the main contributions, methods, challenges, and highlights of the latest and relevant studies using DRL and IRL for robotic manipulation. Additionally, summary tables regarding the problem and solution are presented. The literature review then focuses on the concepts of trustworthy AI, interpretable AI, and explainable AI (XAI) in the context of robotic manipulation. Moreover, this review provides a resource for future research on DRL/IRL in trustworthy robotic manipulation.https://ieeexplore.ieee.org/document/10493015/Deep reinforcement learninginverse reinforcement learningrobotic manipulationartificial intelligencetrustworthy AIinterpretable AI |
| spellingShingle | Recep Ozalp Aysegul Ucar Cuneyt Guzelis Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence IEEE Access Deep reinforcement learning inverse reinforcement learning robotic manipulation artificial intelligence trustworthy AI interpretable AI |
| title | Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence |
| title_full | Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence |
| title_fullStr | Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence |
| title_full_unstemmed | Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence |
| title_short | Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence |
| title_sort | advancements in deep reinforcement learning and inverse reinforcement learning for robotic manipulation toward trustworthy interpretable and explainable artificial intelligence |
| topic | Deep reinforcement learning inverse reinforcement learning robotic manipulation artificial intelligence trustworthy AI interpretable AI |
| url | https://ieeexplore.ieee.org/document/10493015/ |
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