research-article

Domain of Attraction Expansion for Physics-Based Character Control

Authors:

Mazen Al Borno,

Michiel Van De Panne,

Eugene FiumeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 36, Issue 4

Article No.: 145c, Page 1

https://doi.org/10.1145/3072959.3009907

Published: 16 July 2017 Publication History

Abstract

Determining effective control strategies and solutions for high-degree-of-freedom humanoid characters has been a difficult, ongoing problem. A controller is only valid for a subset of the states of the character, known as the domain of attraction (DOA). This article shows how many states that are initially outside the DOA can be brought inside it. Our first contribution is to show how DOA expansion can be performed for a high-dimensional simulated character. Our second contribution is to present an algorithm that efficiently increases the DOA using random trees that provide denser coverage than the trees produced by typical sampling-based motion-planning algorithms. The trees are constructed offline but can be queried fast enough for near-real-time control. We show the effect of DOA expansion on getting up, crouch-to-stand, jumping, and standing-twist controllers. We also show how DOA expansion can be used to connect controllers together.

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Cited By

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Index Terms

Domain of Attraction Expansion for Physics-Based Character Control
1. Computing methodologies
  1. Computer graphics
    1. Animation

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 36, Issue 4

August 2017

2155 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3072959

Issue’s Table of Contents

Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 16 July 2017

Accepted: 01 December 2016

Revised: 01 November 2016

Received: 01 November 2015

Published in TOG Volume 36, Issue 4

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Cited By

Tao TWilson MGou Rvan de Panne M(2022)Learning to Get UpACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530697(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530697
Xu PKaramouzas I(2021)A GAN-Like Approach for Physics-Based Imitation Learning and Interactive Character ControlProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801484:3(1-22)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3480148
Quigley ELin WZhu YFedkiw R(2021)Three Dimensional Reconstruction of Botanical Trees with Simulatable GeometryProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801464:3(1-16)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3480146
Park SJang DLee S(2021)Diverse Motion Stylization for Multiple Style Domains via Spatial-Temporal Graph-Based Generative ModelProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801454:3(1-17)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3480145
Kim NLing HXie Zvan de Panne M(2021)Flexible Motion Optimization with Modulated Assistive ForcesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801444:3(1-25)Online publication date: 27-Sep-2021
https://dl.acm.org/doi/10.1145/3480144
Sheen SLarionov EPai D(2021)Volume Preserving Simulation of Soft Tissue with SkinProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34801434:3(1-23)Online publication date: 27-Sep-2021
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Schreiner PPerepichka MLewis HDarkner SKry PErleben KZordan V(2021)Global Position Prediction for Interactive Motion CaptureProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/34799854:3(1-16)Online publication date: 27-Sep-2021
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Kwon TLee YVan De Panne M(2020)Fast and flexible multilegged locomotion using learned centroidal dynamicsACM Transactions on Graphics10.1145/3386569.339243239:4(46:1-46:17)Online publication date: 12-Aug-2020
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