NDLI: Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

Content Provider	IEEE Xplore Digital Library
Author	Clune, J. Beckmann, B.E. Ofria, C. Pennock, R.T.
Copyright Year	2009
Description	Author affiliation: Department of Computer Science and Engineering at Michigan State University (MSU) in East Lansing, USA (Clune, J.; Beckmann, B.E.; Ofria, C.; Pennock, R.T.)
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.
Starting Page	2764
Ending Page	2771
File Size	984790
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983289
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Encoding Robot kinematics Legged locomotion Mobile robots Navigation Evolutionary computation Neural networks Genetics Leg Control systems
Content Type	Text
Resource Type	Article

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