Abstract
Automatically optimizing robotic behavior to solve complex tasks has been one of the main, long-standing goals of Evolutionary Robotics (ER). When successful, this approach will likely fundamentally change the rate of development and deployment of robots in everyday life. Performing this optimization on real robots can be risky and time consuming. As a result, much of the work in ER is done using simulations which can operate many times faster than realtime. The only downside of this, is that, due to the limited fidelity of the simulated environment, the optimized robotic behavior is typically different when transferred to a robot in the real world. This difference is referred to as the reality gap...
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