Generating gaits for physical quadruped robots: evolved neural networks vs. local parameterized search

Authors:
Jason Yosinski;Jeff Clune;Diana Hidalgo;Sarah Nguyen;Juan Cristobal Zagal;Hod Lipson
Affiliations:
Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;University of Chile, Santiago, Chile;Cornell University, Ithaca, NY, USA
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

Citing 4
Cited 0

A hypercube-based encoding for evolving large-scale neural networks

Artificial Life
The sensitivity of HyperNEAT to different geometric representations of a problem

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Autonomous evolution of dynamic gaits with two quadruped robots

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Creating gaits for legged robots is an important task to enable robots to access rugged terrain, yet designing such gaits by hand is a challenging and time-consuming process. In this paper we investigate various algorithms for automating the creation of quadruped gaits. Because many robots do not have accurate simulators, we test gait learning algorithms entirely on a physical robot. We compare the performance of two classes of learning gaits: locally searching parameterized motion models and evolving artificial neural networks with the HyperNEAT generative encoding. All parameter search methods outperform a manually-designed reference gait, but HyperNEAT performs better still, producing gaits nearly 9 times faster than the reference gait.