A step toward evolving biped walking behavior through indirect encoding
Abstract
Teaching simulated biped robots to walk is a popular problem in machine learning. However, until this thesis, evolving a biped controller has not been attempted through an indirect encoding, i.e. a compressed representation of the solution, despite the fact that natural bipeds such as humans evolved through such an indirect encoding (i.e. DNA). Thus the promise for indirect encoding is to evolve gaits that rival those seen in nature. In this thesis, an indirect encoding called HyperNEAT evolves a controller for a biped robot in a computer simulation. To most effectively explore the deceptive behavior space of biped walkers, novelty search is applied as a fitness metric. The result is that although the indirect encoding can evolve a stable bipedal gait, the overall neural architecture is brittle to small mutations. This result suggests that some capabilities might be necessary to include beyond indirect encoding, such as lifetime adaptation. Thus this thesis provides fresh insight into the requisite ingredients for the eventual achievement of fluid bipedal walking through artificial evolution.
Notes
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Thesis Completion
2010
Semester
Spring
Advisor
Stanley, Kenneth O.
Degree
Bachelor of Science (B.S.)
College
College of Engineering and Computer Science
Degree Program
Computer Science
Subjects
Dissertations, Academic -- Electrical Engineering and Computer Science;Electrical Engineering and Computer Science -- Dissertations, Academic
Format
Identifier
DP0022565
Language
English
Access Status
Open Access
Length of Campus-only Access
None
Document Type
Honors in the Major Thesis
Recommended Citation
Olson, Randal S., "A step toward evolving biped walking behavior through indirect encoding" (2010). HIM 1990-2015. 1036.
https://stars.library.ucf.edu/honorstheses1990-2015/1036