Evolutionary Robotics and the Radical Envelope-of-Noise Hypothesis

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Abstract
For several years now, various researchers have endeavored to apply artificial evolution to the automatic design of control systems for real robots. One of the major challenges they face concerns the question of how to assess the fitness of evolving controllers when each evolutionary run typically involves hundreds of thousands of such assessments. This article outlines new ways of thinking about and building simulations upon which such assessments can be performed. It puts forward sufficient conditions for the successful transfer of evolved controllers from simulation to reality and develops a potential methodology for building simulations in which evolving controllers are forced to satisfy these conditions if they are to be reliably fit. It is hypothesized that as long as simulations are built according to this methodology, it does not matter how inaccurate or incomplete they are: Controllers that have evolved to be reliably fit in simulation still will transfer into reality. Two sets of experiments are reported, both of which involve minimal look-up table-based simulations built according to these guidelines. In the first set, controllers were evolved that allowed a Khepera robot to perform a simple memory task in the real world. In the second set, controllers were evolved for the Sussex University gantry robot that were able to distinguish visually a triangle from a square, under extremely noisy real-world conditions, and to steer the robot toward the triangle. In both cases, controllers that were reliably fit in simulation displayed extremely robust behavior when downloaded into reality.

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