neural networks research group
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Modular Neuroevolution for Multilegged Locomotion
Active from 2007 - 2012
Legged robots are useful in tasks such as search and rescue because they can effectively navigate on rugged terrain. However, it is difficult to design controllers for them that would be stable and robust. Learning the control behavior is difficult because optimal behavior is not known, and the search space is too large for reinforcement learning and for straightforward evolution. As a solution, this project proposes a modular approach for evolving neural network controllers for such robots. The search space is effectively reduced by exploiting symmetry in the robot morphology, and encoding it into network modules. Experiments involving physically realistic simulations of a quadruped robot produce the same symmetric gaits, such as pronk, pace, bound and trot, that are seen in quadruped animals. Moreover, the robot can transition dynamically to more effective gaits when faced with obstacles. The modular approach also scales well when the number of legs or their degrees of freedom are increased. Evolved non-modular controllers, in contrast, produce gaits resembling crippled animals that are much less effective and do not scale up as a result. Hand-designed controllers are also less effective, especially on an obstacle terrain. These results suggest that the modular approach is effective for designing robust locomotion controllers for multilegged robots.
People
Vinod Valsalam
Ph.D. Alumni
vkv [at] alumni utexas net
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
Demos
Evolving Controllers for Physical Multilegged Robots
Vinod Valsalam
2011
Evolving Controller Symmetry for Multilegged Robots
Vinod Valsalam
2010
Modular Neuroevolution for Multilegged Locomotion
Vinod Valsalam
2008
Publications
Constructing Controllers for Physical Multilegged Robots using the ENSO Neuroevolution Approach
Vinod K. Valsalam, Jonathan Hiller, Robert MacCurdy, Hod Lipson and Risto Miikkulainen
Evolutionary Intelligence
, 5(1):1--12, 2012.
2012
Evolving Symmetry for Modular System Design
Vinod K. Valsalam and Risto Miikkulainen
IEEE Transactions on Evolutionary Computation
, 15(3):368--386, 2011.
2011
Utilizing Symmetry in Evolutionary Design
Vinod Valsalam
PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 2010. Te...
2010
Evolving Symmetric and Modular Neural Network Controllers for Multilegged Robots
Vinod K. Valsalam and Risto Miikkulainen
In
xploring New Horizons in Evolutionary Design of Robots: Workshop at the 2009 IEEE/RSJ Internat...
2009
Evolving Symmetric and Modular Neural Networks for Distributed Control
Vinod K. Valsalam and Risto Miikkulainen
In
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)
, 731--738, 200...
2009
Modular Neuroevolution for Multilegged Locomotion
Vinod K. Valsalam and Risto Miikkulainen
In
Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008
, 265-272, Ne...
2008
Software/Data
ENSO
This package contains software implementing the ENSO approach for evolving symmetric modular neural networks. It also in...
2010