Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Review
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Deep learning has provided new ways of manipulating, processing and analyzing data. It sometimes may achieve results comparable to, or surpassing human expert performance, and has become a source of inspiration in the era of artificial intelligence. Another subfield of machine learning named reinforcement learning, tries to find an optimal behavior strategy through interactions with the environment. Combining deep learning and reinforcement learning permits resolving critical issues relative to the dimensionality and scalability of data in tasks with sparse reward signals, such as robotic manipulation and control tasks, that neither method permits resolving when applied on its own. In this paper, we present recent significant progress of deep reinforcement learning algorithms, which try to tackle the problems for the application in the domain of robotic manipulation control, such as sample efficiency and generalization. Despite these continuous improvements, currently, the challenges of learning robust and versatile manipulation skills for robots with deep reinforcement learning are still far from being resolved for real-world applications.
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- Authors
- Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., & Dresp-Langley, B.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- ICube Lab, Robotics Department, Strasbourg University, UMR 7357 CNRS, 67085 Strasbourg, France
- ICube Lab, UMR 7357, Centre National de la Recherche Scientifique CNRS, 67085 Strasbourg, France
- Corresponding author
- Liu, Rongrong
- Venue
- Robotics
- Publisher
- MDPI AG
- Published
- 2021-01-24
- Volume
- 10
- Issue
- 1
- Pages
- 22
- ISSN
- 2218-6581 (ISSN-L)
- OpenAlex
- W3122928565
- Cited by
- 188
- References
- 46
- Citations by year
- 2026: 16 · 2025: 52 · 2024: 51 · 2023: 40 · 2022: 22 · 2021: 7
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Robotics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Université de Strasbourg
- Centre National de la Recherche Scientifique
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Keywords
- deep reinforcement learning, robotic manipulation, robot control, sample efficiency, generalization, manipulative skills
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