An Behavior-based Robotics
Developing Intelligent Wheelchairs for the Handicapped
Assistive Technology and Artificial Intelligence, Applications in Robotics, User Interfaces and Natural Language Processing
Cellular encoding for interactive evolutionary robotics
Cellular encoding for interactive evolutionary robotics
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Accelerating neuroevolutionary methods using a Kalman filter
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Evolving a monolithic solution for complex robotic problems is hard. One of the reasons for this is the difficulty of defining a global fitness function that leads to a solution with desired operating properties. The problem with a global fitness function is that it may not reward intermediate solutions that would ultimately lead to the desired operating properties. A possible way to solve such a problem is to decompose the solution space into smaller subsolutions with lower number of intrinsic dimensions. In this paper, we apply the design principles of behavior based systems to decompose a complex robot control task into subsolutions and show how to incrementally modify the fitness function that (1) results in desired operating properties as the subsolutions are learned, and (2) avoids the need to learn the coordination of behaviors separately. We demonstrate our method by learning to control a quadrocopter flying vehicle.