Animation generation for object transportation with a rope using deep reinforcement learning

Sai Keung Wong*, Xu Tao Wei

*此作品的通信作者

研究成果: Article同行評審

摘要

This article presents a reinforcement learning-based approach to generate animation in which two agents use a rope to collaboratively transport a block. The challenge is that the agents need to master several skills, including approaching the block, using the rope to wrap around it, and then moving the block to a predefined goal position. We propose several reward terms to learn the transportation policy and the adjustment policy that govern the skills of the agents. Experiment results showed that the proposed approach was able to generate various animations in different settings, including rope lengths, block sizes, and block shapes. An ablation test revealed the effects of the reward terms. We also investigated factors that affected the performance of the two policies.

原文English
文章編號e2168
期刊Computer Animation and Virtual Worlds
34
發行號3-4
DOIs
出版狀態Published - 1 5月 2023

指紋

深入研究「Animation generation for object transportation with a rope using deep reinforcement learning」主題。共同形成了獨特的指紋。

引用此