Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
A method for isolating morphological effects on evolved behaviour
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Cellular encoding for interactive evolutionary robotics
Cellular encoding for interactive evolutionary robotics
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Evolving 3d morphology and behavior by competition
Artificial Life
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Concept evaluation of a new biologically inspired robot "Littleape"
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Development of the six-legged walking and climbing robot SpaceClimber
Journal of Field Robotics
A species-based approach to brain-body co-evolution of modular robots
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Neural Networks
EDHMoR: Evolutionary designer of heterogeneous modular robots
Engineering Applications of Artificial Intelligence
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In this work, we describe a learning system that uses the CMA-ES method from evolutionary computation to optimize the morphology and the walking patterns for a complex legged robot simultaneously. Using simulation tools has the advantage that an optimization of robot morphology is possible before actually building the robot. Also, manually developing walking patterns for kinematically complex robots can be a challenging and time-consuming task. Both, the walking pattern and the morphology depend highly on each other to produce an energy-efficient and stable locomotion behaviour. In order to automate this design process, a learning system that generates, tests, and optimizes different walking patterns and morphologies is needed, as well as the ability to accurately simulate a robot and its environment. The evolutionary algorithm optimizes parameters that affect the trajectories of the robot's foot points, testing the resulting walking patterns in a physical simulation. The robot's limbs are controlled using inverse kinematics. In the future, the best solution evolved by this approach will be used for the mechanical construction of the real robot. Afterwards, the optimized walking patterns will be transferred to the real robot.