The society of mind
Biologically inspired robot behavior engineering
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
COOPERATIVE COEVOLUTION OF MULTI-AGENT SYSTEMS
Incremental Evolution of Complex General Behavior
Incremental Evolution of Complex General Behavior
Solving non-Markovian control tasks with neuroevolution
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Highly modular architecture for the general control of autonomous robots
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Artificial Life
Modular Reactive Neurocontrol for Biologically Inspired Walking Machines
International Journal of Robotics Research
Modular neuroevolution for multilegged locomotion
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Tactical modularity for evolutionary animats
Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Tactical Modularity in Cognitive Systems
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Embodying cognitive abilities: categorization
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Confronting the challenge of learning a flexible neural controller for a diversity of morphologies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Single-unit pattern generators for quadruped locomotion
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper describes how a distributed neural architecture for the general control of robots has been applied for the generation of a walking behaviour in the Aibo robotic dog. The architecture described has been already demonstrated useful for the generation of more simple behaviours like standing or standing up. This paper describes specifically how it has been applied to the generation of a walking pattern in a quadruped with twelve degrees of freedom, in both simulator and real robot. The main target of this paper is to show that our distributed architecture can be applied to complex dynamic tasks like walking. Nevertheless, by showing this, we also show how a completely neural and distributed controller can be obtained for a robot as complex as Aibo on a task as complex as walking. This second result is by itself a new and interesting one since, to our extent, there are no other completely neural controllers for quadruped with so many DOF that allow the robot to walk. Bio-inspiration is used in three ways: first we use the concept of central pattern generators in animals to obtain the desired walking robot. Second we apply evolutionary processes to obtain the neural controllers. Third, we seek limitations in how real dogs do walk in order to apply them to our controller and limit the search space.