Abstract
Reflexes are meant to provide animals with automatic responses for a better adaptation to their niches. In particular, humans have the capability to voluntarily modify these responses in certain situations to attain specific goals. The ability of using past experiences to tune automatic responses (reflexes) has contributed to a better adaptation to our environments and thus, the question arises of applying this to machines. In the robotic arena, imitating animal reflexes has been largely explored through fixed stimuli-behavior schemas included in reactive or hybrid architectures. In this paper we consider the less explored direction of permitting a mobile robot to modify its reflexes according to its experience, i.e. ignoring the reflex of stopping when approaching an obstacle if the robot goal is close. We explore reinforcement learning as a mechanism to automatically learn when and how modulate reflexes over the robot operational life. Advantages of our mechanism are illustrated in simulations.
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Galindo, C., Fernández-Madrigal, J.A., González, J. (2007). Towards the Automatic Learning of Reflex Modulation for Mobile Robot Navigation. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_37
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DOI: https://doi.org/10.1007/978-3-540-73055-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73054-5
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