The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Safe controller optimization for quadrotors with Gaussian processes
Open accessAbstract
One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.
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Self-citationBayesian Optimization with Unknown Constraints
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Cited by
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 835
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics
Berkenkamp, F., Krause, A., & Schoellig, A. P. (2023) · Machine Learning · doi:10.1007/s10994-021-06019-1 · Cited by 286
Self-citationA Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials
Chatzilygeroudis, K., Vassiliades, V., Stulp, F., Calinon, S., & Mouret, J.-B. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2958211 · Cited by 160
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.034 · Cited by 74
Bayesian optimization for maximum power point tracking in photovoltaic power plants
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Self-citation
Metadata
- Authors
- Berkenkamp, F., Schoellig, A. P., & Krause, A.
- Venue
- 2016 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 491-496
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2143277772
- Cited by
- 264
- References
- 20
- FWCI
- 39.67
- Type
- conference paper
- Language
- en
- Open access
- green
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- Berkenkamp, F., Schoellig, A. P., & Krause, A. (2016). Safe controller optimization for quadrotors with Gaussian processes. 2016 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2016.7487170
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