Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations
DOI:
https://doi.org/10.19139/soic-2310-5070-1797Keywords:
Reinforcement Learning, Robotic GraspingAbstract
This review paper provides a comprehensive analysis of over 100 research papers focused on the challenges of robotic grasping and the effectiveness of various machine learning techniques, particularly those utilizing Deep Neural Networks (DNNs) and Reinforcement Learning (RL). The objective of this review is to simplify the research process for others by gathering different forms of Deep Reinforcement Learning (DRL) grasping tasks in one place. Through a thorough analysis of the literature, the study emphasizes the critical nature of grasping for robots and how DRL techniques, particularly the Soft-Actor-Critic (SAC) strategy, have demonstrated high efficiency in handling the task. The results of this study hold significant implications for the development of more advanced and efficient grasping systems for robots. Continued research in this area is crucial to further enhance the capabilities of robots in handling complex and challenging tasks, such as grasping.Downloads
Published
2023-12-19
How to Cite
Sekkat, H., Moutik, O., Ourabah, L., Elkari, B., Chaibi, Y. ., & Ait Tchakoucht, T. (2023). Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations. Statistics, Optimization & Information Computing, 12(2), 571–601. https://doi.org/10.19139/soic-2310-5070-1797
Issue
Section
Research Articles
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
