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

Print

Identifier

DP0022565

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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