Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

  • Authors:

  • Jeff Clune;Benjamin E. Beckmann;Charles Ofria;Robert T. Pennock

  • Affiliations:

  • Department of Computer Science and Engineering, Michigan State University, East Lansing, MI;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI and Department of Philosophy and Lyman Briggs College, MSU

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

Quantified Score

Hi-index 0.02

Visualization

Abstract

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 problem's geometric symmetries, which may aid its performance. We compare HyperNEAT to FT-NEAT, a direct encoding control, and find that HyperNEAT is able to evolve impressive quadruped gaits and vastly outperforms FT-NEAT. Comparative analyses reveal that HyperNEAT individuals are more holistically affected by genetic operators, resulting in better leg coordination. Overall, the results suggest that HyperNEAT is a powerful algorithm for evolving control systems for complex, yet regular, devices, such as robots.