101256 20220405150417.0 doi 10.1109/TASE.2020.2980246 sideral 117972 ART-2020-117972 eng Azagra, Pablo Universidad de Zaragoza (orcid)0000-0002-3567-3294 Incremental Learning of Object Models From Natural Human-Robot Interactions 2020 Access copy available to the general public Unrestricted In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild, incrementally, and through their interactions with humans. This article presents an end-to-end pipeline to learn object models incrementally during the human-robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines. info:eu-repo/grantAgreement/ES/DGA/T45-17R info:eu-repo/grantAgreement/EC/FP7/248663/EU/European Coordinated Research on Long-term Challenges in Information and Communication Sciences and Technologies/CHIST- ERA info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-096367-B-I00 info:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 5.083 2020 AUTOMATION & CONTROL SYSTEMS 16 / 63 = 0.254 2020 Q2 T1 1.314 2020 Electrical and Electronic Engineering 2020 Q1 Control and Systems Engineering 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Civera, Javier Universidad de Zaragoza (orcid)0000-0003-1368-1151 Murillo, Ana C. Universidad de Zaragoza (orcid)0000-0002-7580-9037 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 17, 4 (2020), 1883 - 1900 IEEE Trans. Autom. Sci. Eng. IEEE Transactions on Automation Science and Engineering 1545-5955 12099107 http://zaguan.unizar.es/record/101256/files/texto_completo.pdf Postprint 3063519 http://zaguan.unizar.es/record/101256/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:101256 articulos driver 2022-04-05-14:37:51 ARTICLE