Evolving robot gaits in hardware: The HyperNEAT generative encoding vs. parameter optimization

Author(s): 
Yosinski J
Clune J
Hidalgo D
Nguyen S
Cristobal Zagal J
Lipson H
Year: 
2011
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 gait-learning algorithms: locally searching parameterized motion models and evolving artificial neural networks with the HyperNEAT generative encoding. Specifically, we test six different parameterized learning strategies: uniform and Gaussian random hill climbing, policy gradient reinforcement learning, Nelder-Mead simplex, a random baseline, and a new method that builds a model of the fitness landscape with linear regression to guide further exploration. While all parameter search methods outperform a manually-designed gait, only the linear regression and Nelder-Mead simplex strategies outperform a random baseline strategy. Gaits evolved with HyperNEAT perform considerably better than all parameterized local search methods and produce gaits nearly 9 times faster than a hand-designed gait. The best HyperNEAT gaits exhibit complex motion patterns that contain multiple frequencies, yet are regular in that the leg movements are coordinated.


Evolving Gaits for Legged Robots: Neural Networks with Geometric Patterns Perform Better

Neural networks evolved to produce gaits for legged robots. The use of the HyperNEAT generative encoding produces geometric patterns (regularities) in the neural wiring of the evolved brains, which improves performance by producing coordinated, regular leg movements.

Evolving artificial neural networks (ANNs) and gaits for robots are difficult, time-consuming tasks for engineers, making them suitable for evolutionary algorithms (aka genetic algorithms). Generative encodings (aka indirect and developmental encodings) perform better than direct encodings by producing neural regularities that result in behavioral regularities.

Evolving Gaits for Physical Robots Directly in Hardware with the HyperNEAT Generative Encoding

Some of the gaits evolved by the HyperNEAT algorithm.

Evolving Gaits for Legged Robots: Neural Networks with Geometric Patterns Perform Better

Neural networks evolved to produce gaits for legged robots. The use of the HyperNEAT generative encoding produces geometric patterns (regularities) in the neural wiring of the evolved brains, which improves performance by producing coordinated, regular leg movements.

Evolving Gaits for Physical Robots Directly in Hardware with the HyperNEAT Generative Encoding

Some of the gaits evolved by the HyperNEAT algorithm.

Pub. Info: 
Proceedings of the European Conference on Artificial Life. 890-897
BibTeX: 

@INPROCEEDINGS{Yosinski11evolvingrobot,
author = {Jason Yosinski and Jeff Clune and Diana Hidalgo and Sarah Nguyen and Juan Cristobal Zagal and Hod Lipson},
title = {Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization},
booktitle = {In Proceedings of the 20th European Conference on Artificial Life},
year = {2011},
pages = {890--897}
}