Abstract
Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.
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Tan, C.K., Plöger, P.G., Trappenberg, T.P. (2016). A Neural Field Approach to Obstacle Avoidance. In: Friedrich, G., Helmert, M., Wotawa, F. (eds) KI 2016: Advances in Artificial Intelligence. KI 2016. Lecture Notes in Computer Science(), vol 9904. Springer, Cham. https://doi.org/10.1007/978-3-319-46073-4_6
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DOI: https://doi.org/10.1007/978-3-319-46073-4_6
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