Abstract
Physics-based character animation offers an attractive alternative for traditional animations. However, it is often strenuous for a physics-based approach to incorporate active user control of different characters. In this paper, a neuroevolutionary approach is proposed using HyperNEAT to combine individually trained neural controllers to form a control strategy for a simulated eight-legged character, which is a previously untested character morphology for this algorithm. It is aimed to evaluate the robustness and responsiveness of the control strategy that changes the controllers based on simulated user inputs. The experiment result shows that HyperNEAT is able to evolve long walking controllers for this character. In addition, it also suggests a requirement for further refinement when operated in tandem.
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Albrigtsen, S.I., Imenes, A., Goodwin, M., Jiao, L., Nunavath, V. (2018). Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character. In: Pimenidis, E., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2018. Communications in Computer and Information Science, vol 893. Springer, Cham. https://doi.org/10.1007/978-3-319-98204-5_8
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