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

• Published in: Computer animation and virtual worlds Vol. 32; no. 3-4
• Main Authors: Chen, Shao‐Chieh, Liu, Guan‐Ting, Wong, Sai‐Keung
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.06.2021
• Subjects: Animation; blend‐tree; Deep learning; Locomotion; multiagent; Multiagent systems; object transportation; Policies; reinforcement learning; Transportation; Trees
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.2017

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. We propose 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. 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.