research-article

Space-time planning with parameterized locomotion controllers

Authors:

Vladlen Koltun,

Zoran PopovićAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 30, Issue 3

Article No.: 23, Pages 1 - 11

https://doi.org/10.1145/1966394.1966402

Published: 19 May 2011 Publication History

Abstract

We present a technique for efficiently synthesizing animations for characters traversing complex dynamic environments. Our method uses parameterized locomotion controllers that correspond to specific motion skills, such as jumping or obstacle avoidance. The controllers are created from motion capture data with reinforcement learning. A space-time planner determines the sequence in which controllers must be executed to reach a goal location, and admits a variety of cost functions to produce paths that exhibit different behaviors. By planning in space and time, the planner can discover paths through dynamically changing environments, even if no path exists in any static snapshot. By using parameterized controllers able to handle navigational tasks, the planner can operate efficiently at a high level, leading to interactive replanning rates.

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Cited By

Modi VChen YMadan ASueda SLevin D(2023)Multi‐agent Path Planning with Heterogenous Interactions in Tight SpacesComputer Graphics Forum10.1111/cgf.1473742:6Online publication date: 28-Feb-2023
https://doi.org/10.1111/cgf.14737
Averta GAverta G(2022)Exploiting Principal Components for Robots Walking: An Approach for Sub-Optimal LocomotionHuman-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices10.1007/978-3-030-92521-5_14(255-273)Online publication date: 25-Jan-2022
https://doi.org/10.1007/978-3-030-92521-5_14
Ao SZhou TLong GLu QZhu LJiang JRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)CO-PILOTProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541060(10444-10456)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541060
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Index Terms

Space-time planning with parameterized locomotion controllers
1. Computing methodologies
  1. Computer graphics
    1. Animation

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 30, Issue 3

May 2011

127 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1966394

Issue’s Table of Contents

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 19 May 2011

Revised: 01 December 2010

Received: 01 July 2010

Accepted: 01 February 2010

Published in TOG Volume 30, Issue 3

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Cited By

Modi VChen YMadan ASueda SLevin D(2023)Multi‐agent Path Planning with Heterogenous Interactions in Tight SpacesComputer Graphics Forum10.1111/cgf.1473742:6Online publication date: 28-Feb-2023
https://doi.org/10.1111/cgf.14737
Averta GAverta G(2022)Exploiting Principal Components for Robots Walking: An Approach for Sub-Optimal LocomotionHuman-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices10.1007/978-3-030-92521-5_14(255-273)Online publication date: 25-Jan-2022
https://doi.org/10.1007/978-3-030-92521-5_14
Ao SZhou TLong GLu QZhu LJiang JRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)CO-PILOTProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541060(10444-10456)Online publication date: 6-Dec-2021
https://dl.acm.org/doi/10.5555/3540261.3541060
Cho KKim CPark JPark JNoh J(2021)Motion recommendation for online character controlACM Transactions on Graphics10.1145/3478513.348051240:6(1-16)Online publication date: 10-Dec-2021
https://dl.acm.org/doi/10.1145/3478513.3480512
Lee KMin SLee SLee J(2021)Learning time-critical responses for interactive character controlACM Transactions on Graphics10.1145/3450626.345982640:4(1-11)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459826
Peng XMa ZAbbeel PLevine SKanazawa A(2021)AMPACM Transactions on Graphics10.1145/3450626.345967040:4(1-20)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459670
Souza CVelhor L(2021)Deep Reinforcement Learning for Task Planning of Virtual CharactersIntelligent Computing10.1007/978-3-030-80126-7_49(694-711)Online publication date: 7-Jul-2021
https://doi.org/10.1007/978-3-030-80126-7_49
Wang PLiu LChen NChu HTheobalt CWang W(2020)Vid2CurveACM Transactions on Graphics10.1145/3386569.339247639:4(132:1-132:12)Online publication date: 12-Aug-2020
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Merel JTunyasuvunakool SAhuja ATassa YHasenclever LPham VErez TWayne GHeess N(2020)Catch & CarryACM Transactions on Graphics10.1145/3386569.339247439:4(39:1-39:12)Online publication date: 12-Aug-2020
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Kim BAzevedo VGross MSolenthaler B(2020)Lagrangian neural style transfer for fluidsACM Transactions on Graphics10.1145/3386569.339247339:4(52:1-52:10)Online publication date: 12-Aug-2020
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