Communications of the ACM
Heterogeneous neural networks for adaptive behavior in dynamic environments
Advances in neural information processing systems 1
Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Automatic definition of modular neural networks
Adaptive Behavior
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Clustering in massive data sets
Handbook of massive data sets
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
Design, simulation, and stability of a hexapedal running robot
Design, simulation, and stability of a hexapedal running robot
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
Evolving the walking behaviour of a 12 DOF quadruped using a distributed neural architecture
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
The sensitivity of HyperNEAT to different geometric representations of a problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving symmetric and modular neural networks for distributed control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Neural control of a modular multi-legged walking machine: Simulation and hardware
Robotics and Autonomous Systems
ARCS'10 Proceedings of the 23rd international conference on Architecture of Computing Systems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Efficient neuroevolution for a quadruped robot
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Single-unit pattern generators for quadruped locomotion
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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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 paper 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.