145677 20260217205521.0 doi 10.1109/LRA.2024.3475914 sideral 140607 ART-2024-140607 eng Lechuz-Sierra, Juan G. Universidad de Zaragoza Bayesian optimization for robust robotic grasping using a sensorized compliant hand 2024 Access copy available to the general public Unrestricted One of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the difficulty lies in adapting the grasping strategies to a large variety of tasks and objects, which can often be unknown. The brute-force solution is to learn new grasps by trial and error, which is inefficient and ineffective. In contrast, Bayesian optimization applies active learning by adding information to the approximation of an optimal grasp. This paper proposes the use of Bayesian optimization techniques to safely perform robotic grasping. We analyze different grasp metrics to provide realistic grasp optimization in a real system including tactile sensors. An experimental evaluation in the robotic system shows the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent to a real-world environment. info:eu-repo/grantAgreement/ES/AEI/PID2020-114819GB-I00 info:eu-repo/grantAgreement/ES/DGA/T45-23R info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101070136/EU/AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics/IntelliMan info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 5.3 2024 ROBOTICS 12 / 48 = 0.25 2024 Q1 T1 1.481 2024 Artificial Intelligence 2024 Q1 Biomedical Engineering 2024 Q1 Computer Science Applications 2024 Q1 Mechanical Engineering 2024 Q1 Control and Optimization 2024 Q1 Control and Systems Engineering 2024 Q1 Human-Computer Interaction 2024 Q1 Computer Vision and Pattern Recognition 2024 Q1 10.3 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Martín, Ana Elvira H. Sundaram, Ashok M. Martinez-Cantín, Rubén Universidad de Zaragoza (orcid)0000-0002-6741-844X Roa, Máximo A. 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 9, 11 (2024), 10503-10510 IEEE Robot. autom. let. IEEE Robotics and Automation Letters 2377-3766 2263152 http://zaguan.unizar.es/record/145677/files/texto_completo.pdf Versión publicada 3318559 http://zaguan.unizar.es/record/145677/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:145677 articulos driver 2026-02-17-20:28:39 ARTICLE