RT Journal Article
T1 Learning on real robots from experience and simple user feedback
A1 Quintía Vidal, Pablo
A1 Iglesias Rodríguez, Roberto
A1 Rodríguez González, Miguel Ángel
A1 Vázquez Regueiro, Carlos
K1 Autonomous robots
K1 Reinforcement learning
AB In this article we describe a novel algorithm that allows fast and continuous learning on a physical robot working in a real environment. The learning process is never stopped and new knowledge gained from robot-environment interactions can be incorporated into the controller at any time. Our algorithm lets a human observer control the reward given to the robot, hence avoiding the burden of defining a reward function. Despite the highly-non-deterministic reinforcement, through the experimental results described in this paper, we will see how the learning processes are never stopped and are able to achieve fast robot adaptation to the diversity of different situations the robot encounters while it is moving in several environments
PB Universitat d'Alacant
SN 1888-0258
YR 2013
FD 2013
LK http://hdl.handle.net/10347/17703
UL http://hdl.handle.net/10347/17703
LA eng
NO Quintía Vidal, P., Iglesias Rodríguez, R., Rodríguez González, M., & Vázquez Regueiro, C. (2013). Learning on real robots from experience and simple user feedback. Journal of Physical Agents, 7(1), 57-65. doi:https://doi.org/10.14198/JoPha.2013.7.1.08
NO This work was supported by the research grant TIN2009-07737 of the Spanish Ministerio de Economa y Competitividad, and María Barbeito program of the Xunta de Galicia
DS Minerva
RD 24 abr 2026