Learning Climbing Controllers for Physics-Based Characters
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Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We propose a physics-based climbing controller that consists of two learning stages. Firstly, a hanging policy is trained to grasp holds in a natural posture. Once the policy is obtained, it is used to extract the positions of the holds, postures, and grip states, thus forming a dataset of favorable hanging poses. Subsequently, a climbing policy is trained to execute actual climbing maneuvers using this hanging state dataset. The climbing policy allows the character to move to the target location using limbs more evenly. Experiments have shown that the proposed method can effectively explore the space of good postures for climbing.
Description
CCS Concepts: Computing methodologies → Physical simulation; Motion capture; Reinforcement learning
@inproceedings{10.2312:sca.20241165,
booktitle = {Eurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters},
editor = {Zordan, Victor},
title = {{Learning Climbing Controllers for Physics-Based Characters}},
author = {Kang, Kyungwon and Gu, Taehong and Kwon, Taesoo},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-263-9},
DOI = {10.2312/sca.20241165}
}