Generation of multiagent animation for object transportation using deep reinforcement learning and blend-trees

Shao Chieh Chen, Guan Ting Liu, Sai-Keung Wong*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper proposes a framework that integrates reinforcement learning and blend-trees to generate animation of multiple agents for object transportation. The main idea is that in the learning stage, policies are learned to control agents to perform specific skills, including navigation, pushing, and orientation adjustment. The policies determine the blending parameters of the blend-trees to achieve locomotion control of the agents. In the simulation stage, the policies are combined to control the agents to navigate, push objects, and adjust orientation of the objects. We demonstrated several examples to show that the framework is capable of generating animation of multiple agents in different scenarios.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalComputer Animation and Virtual Worlds
Volume32
Issue number3-4
DOIs
StatePublished - Jun 2021

Keywords

  • animation
  • blend-tree
  • multiagent
  • object transportation
  • reinforcement learning

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