Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations


Mohammed, Marwan Qaid and Kwek, Lee Chung and Chua, Shing Chyi (2020) Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations. IEEE Access, 8. pp. 178450-178481. ISSN 2169-3536

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines

Item Type: Article
Uncontrolled Keywords: Object-oriented methods (Computer science), Deep reinforcement learning, object manipulation, robotic grasping.
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 08 Sep 2021 23:40
Last Modified: 08 Sep 2021 23:40


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