Editorial: Cognitive inspired aspects of robot learning
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Archivos
Fecha
2023
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
Frontiers Media SA
Nombre de Curso
Licencia CC
CC BY 4.0 DEED Attribution 4.0 International
Licencia CC
https://creativecommons.org/licenses/by/4.0/
Resumen
Robot 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).
Notas
Indexación: Scopus.
Palabras clave
Bio-inspired robotics, Cognitive robotics, Robot learning and behavior adaptation, Robotics, Social robotics/HRI engineers
Citación
Frontiers in Neurorobotics. Volume 17. 2023. Article number 1256788
DOI
10.3389/fnbot.2023.1256788