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Träfflista för sökning "WFRF:(Wachel Pawe L.) "

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1.
  • Baumann, Dominik, Ph.D. 1991-, et al. (author)
  • A computationally lightweight safe learning algorithm
  • 2023
  • In: 2023 62nd IEEE Conference on Decision and Control, (CDC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350301243 - 9798350301250 ; , s. 1022-1027
  • Conference paper (peer-reviewed)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.
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2.
  • Wachel, Pawe l, et al. (author)
  • Decentralized diffusion-based learning under non-parametric limited prior knowledge
  • 2024
  • In: European Journal of Control. - : Elsevier BV. - 0947-3580 .- 1435-5671. ; 75
  • Journal article (peer-reviewed)abstract
    • We study the problem of diffusion-based network learning of a nonlinear phenomenon, m, from local agents' measurements collected in a noisy environment. For a decentralized network and information spreading merely between directly neighboring nodes, we propose a non-parametric learning algorithm, that avoids raw data exchange and requires only mild a priori knowledge about m. Non-asymptotic estimation error bounds are derived for the proposed method. Its potential applications are illustrated through simulation experiments.
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