Coordination of leg movement in walking animals
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolving organisms that can reach for objects
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Evolving mobile robots in simulated and real environments
Artificial Life
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Evolutionary design of morphology and intelligence in robotic system using genetic programming
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Understanding intelligence
Multi-objectivity for brain-behavior evolution of a physically-embodied organism
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Evolutionary Computation - Special issue on magnetic algorithms
An Agent-Based Evolutionary Robotic System for Its Reconfiguration
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Evolving morphologies and gaits of physically realistic simulated robots
Proceedings of the 2009 ACM symposium on Applied Computing
Robot design for space missions using evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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As the field of embodied cognitive science begins to mature, it is imperative to develop methods for identifying and quantifying the constraints and opportunities an agent's body places on its possible behaviours. In this paper we present results from a set of experiments conducted on 10 different legged agents, in which we evolve neural controllers for locomotion. The genetic algorithm and neural network architecture were kept constant across the agent set, but the agents had different sizes, masses and body plans. It was found that increased mass has a negative effect on the evolution of locomotion, but that this does not hold for all of the agents tested. Also, the number of legs has an effect on evolved behaviours, with hexapedal agents being the easiest for which to evolve locomotion, and wormlike agents being the most difficult. Moreover, it was found that repeating the experiments with a larger neural network increased the evolutionary potential of some of the agents, but not for all of them. The results suggest that by employing this methodology we can test hypotheses about the behavioural effect of specific morphological features, which has to date eluded precise quantitative analysis.