A computationally lightweight safe learning algorithm
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A4 Artikkeli konferenssijulkaisussa
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Date
2024-01-19
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Language
en
Pages
6
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Proceedings of the IEEE Conference on Decision & Control
Abstract
Safety is an essential asset when learning control policies for physical systems, as violating safety constraints during training can lead to expensive hardware damage. In response to this need, the field of safe learning has emerged with algorithms that can provide probabilistic safety guarantees without knowledge of the underlying system dynamics. Those algorithms often rely on Gaussian process inference. Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems. In this paper, we propose a safe learning algorithm that provides probabilistic safety guarantees but leverages the Nadaraya-Watson estimator instead of Gaussian processes. For the Nadaraya-Watson estimator, we can reach logarithmic scaling with the number of data points. We provide theoretical guarantees for the estimates, embed them into a safe learning algorithm, and show numerical experiments on a simulated seven-degrees-of-freedom robot manipulator.Description
Keywords
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
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Citation
Baumann, D, Kowalczyk, K, Tiels, K & Wachel, P 2024, A computationally lightweight safe learning algorithm . in 2023 62nd IEEE Conference on Decision and Control, CDC 2023 . Proceedings of the IEEE Conference on Decision & Control, IEEE, pp. 1022-1027, IEEE Conference on Decision and Control, Singapore, Singapore, 13/12/2023 . https://doi.org/10.1109/CDC49753.2023.10384018