Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

— Legged robots show promise for complex mobility tasks, such as navigating rough terrain, but the design of their control software is both challenging and laborious. Traditional evolutionary algorithms can produce these controllers, but require manual decomposition or other problem simplification because conventionally-used direct encodings have trouble taking advantage of a problem's regularities and symmetries. Such active intervention is time consuming, limits the range of potential solutions, and requires the user to possess a deep understanding of the problem's structure. This paper demonstrates that HyperNEAT, a new and promising generative encoding for evolving neural networks, can evolve quadruped gaits without an engineer manually decomposing the problem. Analyses suggest that HyperNEAT is successful because it employs a generative encoding that can more easily reuse phenotypic modules. It is also one of the first neuroevolutionary algorithms that exploits a probl...

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Jeff Clune, Benjamin E. Beckmann, Charles Ofria, Robert T. Pennock
• Keywords: Artificial Intelligence, CEC 2009, Direct Encoding, Generative Encoding, Quadruped Gaits