The DOI matches its Crossref record; 137 of the 137 references it cites were found in a registry or an index.
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Open accessAbstract
The last half decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. It includes learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximityto humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches.
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Cites
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Cited by
Dawson, C., Gao, S., & Fan, C. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3232542 · Cited by 269
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation
Han, D., Mulyana, B., Stankovic, V., & Cheng, S. (2023) · Sensors · doi:10.3390/s23073762 · Cited by 253
Wabersich, K. P., Taylor, A. J., Choi, J. J., Sreenath, K., Tomlin, C. J., Ames, A. D., & Zeilinger, M. N. (2023) · IEEE Control Systems · doi:10.1109/mcs.2023.3291885 · Cited by 132
Agentic Large Language Models, a Survey
Plaat, A., Van Duijn, M., Van Stein, N., Preuss, M., Van der Putten, P., & Batenburg, K. J. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.18675 · Cited by 75
Robot Model Identification and Learning: A Modern Perspective
Lee, T., Kwon, J., Wensing, P. M., & Park, F. C. (2024) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-061523-102310 · Cited by 34
Metadata
- Authors
- Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P.
- Venue
- Annual Review of Control, Robotics, and Autonomous Systems
- Publisher
- Annual Reviews
- Published
- 2022-05-03
- Volume
- 5
- Issue
- 1
- Pages
- 411-444
- ISSN
- 2573-5144 (ISSN-L)
- OpenAlex
- W3195968524
- Cited by
- 769
- References
- 137
- FWCI
- 11.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Brunke, L., Greeff, M., Hall, A. W., Yuan, Z., Zhou, S., Panerati, J., & Schoellig, A. P. (2022). Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/annurev-control-042920-020211
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
