Abstract :
In this paper, we propose a hierarchical neuroevolution technique for physics-based character animation control. The artificial neural network that makes up the controller is composed of a number of interdependent control modules. As a proof-of-concept, modules for posing, standing, and reaching motions are demonstrated. We show that evolving these modules one-by-one, with each of them dependent on its predecessors, allows evolution to converge faster, and possibly deliver better and more stable results than common, i.e. non-hierarchical, controllers.

Paper :
Short paper presented at the 27th Conference on Computer Animation and Social Agents (CASA) 2014.
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Video :
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