Intelligence without representation
Artificial Intelligence
Evolving visually guided robots
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
An Behavior-based Robotics
Hardware Solutions for Evolutionary Robotics
Proceedings of the First European Workshop on Evolutionary Robotics
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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In previous work [4] a framework was demonstrated that allows an autonomous robot to automatically synthesize physically-realistic models of its own body. Here it is demonstrated how the same approach can be applied to empower a robot to synthesize physically-realistic models of its surroundings. Robots which build numerical or other nonphysical models of their environments are limited in the kinds of predictions they can make about the repercussions of future actions. In this paper it is shown that a robot equipped with a self-made, physicallyrealistic model can extrapolate: a slow-moving robot consistently predicts the much faster top speed at which it can safely drive across a terrain.