Creating long gait animation sequences through Reinforcement Learning

  • Authors:
  • Marco Alamia;N. Alberto Borghese

  • Affiliations:
  • Applied Intelligent Systems Laboratory (AIS-Lab), Department of Computer Science, University of Milano, marco.alamia@studenti.unimi.it, alberto.borghese@unimi.it;Applied Intelligent Systems Laboratory (AIS-Lab), Department of Computer Science, University of Milano, marco.alamia@studenti.unimi.it, alberto.borghese@unimi.it

  • Venue:
  • Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
  • Year:
  • 2011

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Abstract

In this paper we present how, using a careful definition of a state function, long animation sequences can be created joining clips from a database. Each next clip is chosen in real-time by a controller optimizing a cost function on the state function; this allows the user interact in real-time with the digital character. We analyze here two possible cost functions, one that is based on the evaluation of the compatibility of the next clip and one based on reinforcement learning in which the global policy of the controller is evaluated.