Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Evolving Virtual Creatures and Catapults
Artificial Life
Steady-state ALPS for real-valued problems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving 3d morphology and behavior by competition
Artificial Life
Evolution of vision capabilities in embodied virtual creatures
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Guarding against premature convergence while accelerating evolutionary search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Innocent Until Proven Guilty: Reducing Robot Shaping From Polynomial to Linear Time
IEEE Transactions on Evolutionary Computation
Emotion as morphofunctionality
Artificial Life
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Scaffolding---initially simplifying the task environment of autonomous robots---has been shown to increase the probability of evolving robots capable of performing in more complex task environments. Recently, it has been shown that changes to the body of a robot may also scaffold the evolution of non trivial behavior. This raises the question of whether two different kinds of scaffolding (environmental and morphological) synergize with one another when combined. Here it is shown that, for legged robots evolved to perform phototaxis, synergy can be achieved, but only if morphological and environmental scaffolding are combined in a particular way: The robots must first undergo morphological scaffolding, followed by environmental scaffolding. This suggests that additional kinds of scaffolding may create additional synergies that lead to the evolution of increasingly complex robot behaviors.