Editorial: Cognitive inspired aspects of robot learning

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dc.contributor.authorCruz, Francisco
dc.contributor.authorSolis, Miguel A.
dc.contributor.authorNavarro-Guerrero, Nicolás
dc.date.accessioned2024-05-23T22:38:59Z
dc.date.available2024-05-23T22:38:59Z
dc.date.issued2023
dc.descriptionIndexación: Scopus.
dc.description.abstractRobot learning enables robots to acquire new knowledge and skills through experience and interaction with their environment. Robot learning involves developing algorithms that allow robots to learn autonomously, adapt to new situations, and improve their performance over time. Using machine learning, robots can analyze large amounts of data and extract patterns to make decisions. This approach allows robots to learn from past experiences and apply that knowledge to future tasks, ultimately enhancing their capabilities and versatility. However, although machine learning has shown great potential in robot learning, it also faces several challenges and limitations. One significant problem, for instance, is the issue of data scarcity. Collecting sufficient and diverse data for training robots can be complex and time-consuming (Navarro-Guerrero et al., 2023). Unlike traditional machine learning applications where large datasets might be available, gathering data for robot learning often requires physical interactions and real-world environments, which can be expensive and challenging (Navarro-Guerrero et al., 2023).
dc.description.urihttps://www.frontiersin.org/articles/10.3389/fnbot.2023.1256788/full
dc.identifier.citationFrontiers in Neurorobotics. Volume 17. 2023. Article number 1256788
dc.identifier.doi10.3389/fnbot.2023.1256788
dc.identifier.issn1662-5218
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/57033
dc.language.isoen
dc.publisherFrontiers Media SA
dc.rights.licenseCC BY 4.0 DEED Attribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBio-inspired robotics
dc.subjectCognitive robotics
dc.subjectRobot learning and behavior adaptation
dc.subjectRobotics
dc.subjectSocial robotics/HRI engineers
dc.titleEditorial: Cognitive inspired aspects of robot learning
dc.typeArtículo
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