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

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, an...

Full description

Saved in:
Bibliographic Details
Published in:Computer animation and virtual worlds 2021-06, Vol.32 (3-4), p.n/a
Main Authors: Chen, Shao‐Chieh, Liu, Guan‐Ting, Wong, Sai‐Keung
Format: Article
Language:English
Subjects:
Animation
blend‐tree
Deep learning
Locomotion
multiagent
Multiagent systems
object transportation
Policies
reinforcement learning
Transportation
Trees
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1546-4261
1546-427X
DOI:10.1002/cav.2017