000145677 001__ 145677
000145677 005__ 20241122130823.0
000145677 0247_ $$2doi$$a10.1109/LRA.2024.3475914
000145677 0248_ $$2sideral$$a140607
000145677 037__ $$aART-2024-140607
000145677 041__ $$aeng
000145677 100__ $$aLechuz-Sierra, Juan G.$$uUniversidad de Zaragoza
000145677 245__ $$aBayesian optimization for robust robotic grasping using a sensorized compliant hand
000145677 260__ $$c2024
000145677 5060_ $$aAccess copy available to the general public$$fUnrestricted
000145677 5203_ $$aOne of the first tasks we learn as children is to grasp objects based on our tactile perception. Incorporating such skill in robots will enable multiple applications, such as increasing flexibility in industrial processes or providing assistance to people with physical disabilities. However, the difficulty lies in adapting the grasping strategies to a large variety of tasks and objects, which can often be unknown. The brute-force solution is to learn new grasps by trial and error, which is inefficient and ineffective. In contrast, Bayesian optimization applies active learning by adding information to the approximation of an optimal grasp. This paper proposes the use of Bayesian optimization techniques to safely perform robotic grasping. We analyze different grasp metrics to provide realistic grasp optimization in a real system including tactile sensors. An experimental evaluation in the robotic system shows the usefulness of the method for performing unknown object grasping even in the presence of noise and uncertainty inherent to a real-world environment.
000145677 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2020-114819GB-I00$$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101070136/EU/AI-Powered Manipulation System for Advanced Robotic Service, Manufacturing and Prosthetics/IntelliMan$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00
000145677 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000145677 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000145677 700__ $$aMartín, Ana Elvira H.
000145677 700__ $$aSundaram, Ashok M.
000145677 700__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantín, Rubén$$uUniversidad de Zaragoza
000145677 700__ $$aRoa, Máximo A.
000145677 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát.
000145677 773__ $$g9, 11 (2024), 10503-10510$$pIEEE Robot. autom. let.$$tIEEE Robotics and Automation Letters$$x2377-3766
000145677 8564_ $$s2263152$$uhttps://zaguan.unizar.es/record/145677/files/texto_completo.pdf$$yVersión publicada
000145677 8564_ $$s3318559$$uhttps://zaguan.unizar.es/record/145677/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000145677 909CO $$ooai:zaguan.unizar.es:145677$$particulos$$pdriver
000145677 951__ $$a2024-11-22-11:52:26
000145677 980__ $$aARTICLE