Robot Learning Using Gate-Level Evolvable Hardware

Recently there has been a great interest in the design and study of evolvable and autonomous systems in order to control the behavior of physically embedded systems such as a mobile robot. This paper studies an evolutionary navigation system for a mobile robot using an evolvable hardware (EHW) approach. This approach is unique in that it combines learning and evolution, which was usually realized by software, with hardware. It can be regarded as an attempt to make hardware "softer". The task of the mobile robot is to reach a goal represented by a colored ball while avoiding obstacles during its motion. We show that our approach can evolve a set of rules to perform the task successfully. We also show that the evolvable hardware system learned off-line is robust and able to perform the desired behaviors in a more complex environment which is not seen in the learning stage.

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