150246 20251017144611.0 doi 10.1109/LRA.2021.3060408 sideral 124059 ART-2021-124059 eng Garcia-Barcos, J. Universidad de Zaragoza (orcid)0000-0001-7251-0753 Robust Policy Search for Robot Navigation 2021 Complex robot navigation and control problems can be framed as policy search problems. However, interactive learning in uncertain environments can be expensive, requiring the use of data-efficient methods. Bayesian optimization is an efficient nonlinear optimization method where queries are carefully selected to gather information about the optimum location. This is achieved by a surrogate model, which encodes past information, and the acquisition function for query selection. Bayesian optimization can be very sensitive to uncertainty in the input data or prior assumptions. In this letter, we incorporate both robust optimization and statistical robustness, showing that both types of robustness are synergistic. For robust optimization we use an improved version of unscented Bayesian optimization which provides safe and repeatable policies in the presence of policy uncertainty. We also provide new theoretical insights. For statistical robustness, we use an adaptive surrogate model and we introduce the Boltzmann selection as a stochastic acquisition method to have convergence guarantees and improved performance even with surrogate modeling errors. We present results in several optimization benchmarks and robot tasks. info:eu-repo/grantAgreement/ES/MINECO-FEDER/RTI2018-096903-B-I00 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 4.321 2021 ROBOTICS 11 / 30 = 0.367 2021 Q2 T2 2.206 2021 Artificial Intelligence 2021 Q1 Biomedical Engineering 2021 Q1 Mechanical Engineering 2021 Q1 Control and Optimization 2021 Q1 Control and Systems Engineering 2021 Q1 Computer Vision and Pattern Recognition 2021 Q1 8.0 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Martinez-Cantin, R. Universidad de Zaragoza (orcid)0000-0002-6741-844X 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 6, 2 (2021), 2389-2396 IEEE Robot. autom. let. IEEE Robotics and Automation Letters 2377-3766 2217369 http://zaguan.unizar.es/record/150246/files/texto_completo.pdf Versión publicada 3472630 http://zaguan.unizar.es/record/150246/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:150246 articulos driver 2025-10-17-14:17:15 ARTICLE