Date of Completion


Document Type

Honors College Thesis


Computer Science

Thesis Type

Honors College

First Advisor

Josh Bongard


Robotics, Evolutionary Algorithm, Central Pattern Generator, Computer Science


Legged locomotion presents a significant challenge in robotics. Many legged robots accomplish stable movement through models of “central pattern generators (CPGs),” a type of neural circuit which underlies biological rhythms from walking, flying and breathing to patterned cognitive and central nervous system activity. While current CPG models are effective solutions for moving from point A to point B, they have several important drawbacks. These include reliance on complex, specialized neuron models and specific neural topology, which make the system difficult to modify or improve. Artificial CPG design also sacrifices stability for adaptability, as their mechanism largely prevents gait variation. In this work, we used a multi-objective evolutionary algorithm to produce virtual robots able to rhythmically entrain—synchronize footstrikes—to a simple metronome. Robots had an “auditory neuron” to sense metronome strikes and the selection algorithm favored individuals which both traveled away from the origin and demonstrated strong rhythmic alignment. In this paper, we explore what conditions and methods might be conducive to evolving rhythmic entrainment in the spirit of minimal cognition. In addition, we demonstrate evolution of a functional CPG with only a small network of simple tanh neurons and make inferences about its mechanism.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.