Evolutionary Robots with Fast Adaptive Behavior in New Environments

This paper is concerned with adaptation capabilities of evolved neural controllers. A method consisting of encoding a set of local adaptation rules that synapses obey while the robot freely moves in the environment [6] is compared to a standard fixed-weight network. In the experiments presented here, the performance of the robot is measured in environments that are different in significant ways from those used during evolution. The results show that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfers from simulation to reality) and new spatial relationships.


Published in:
From Biology to Hardware
Presented at:
3rd International Conference on Evolvable Systems (ICES'2000)
Year:
2000
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Note:
A. Thompson et al. (eds.)
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 Record created 2006-01-12, last modified 2018-03-17

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