research-article

How to train your dragon: example-guided control of flapping flight

Authors:

Jehee LeeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 36, Issue 6

Article No.: 198, Pages 1 - 13

https://doi.org/10.1145/3130800.3130833

Published: 20 November 2017 Publication History

Abstract

Imaginary winged creatures in computer animation applications are expected to perform a variety of motor skills in a physically realistic and controllable manner. Designing physics-based controllers for a flying creature is still very challenging particularly when the dynamic model of the creatures is high-dimensional, having many degrees of freedom. In this paper, we present a control method for flying creatures, which are aerodynamically simulated, interactively controllable, and equipped with a variety of motor skills such as soaring, gliding, hovering, and diving. Each motor skill is represented as Deep Neural Networks (DNN) and learned using Deep Q-Learning (DQL). Our control method is example-guided in the sense that it provides the user with direct control over the learning process by allowing the user to specify keyframes of motor skills. Our novel learning algorithm was inspired by evolutionary strategies of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to improve the convergence rate and the final quality of the control policy. The effectiveness of our Evolutionary DQL method is demonstrated with imaginary winged creatures flying in a physically simulated environment and their motor skills learned automatically from user-provided keyframes.

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Index Terms

How to train your dragon: example-guided control of flapping flight
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
    2. Machine learning approaches
      1. Neural networks

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 36, Issue 6

December 2017

973 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3130800

Editor:
Kavita Bala

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Copyright © 2017 ACM.

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Publication History

Published: 20 November 2017

Published in TOG Volume 36, Issue 6

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MSIT(Ministry of Science and ICT) of Korea

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Chen QGuo WFang YTong YLu TJin XDeng Z(2025)A Bio-Inspired Model for Bee SimulationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337908031:4(2073-2085)Online publication date: Apr-2025
https://doi.org/10.1109/TVCG.2024.3379080
Jouve JRomero VNarain RBoissieux LKim TBertails-Descoubes F(2024)Modelling a Feather as a Strongly Anisotropic Elastic ShellACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657503(1-10)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657503
Chen QDeng ZLi FFang YLu TTong YZuo Y(2024)Real-time Wing Deformation Simulations for Flying InsectsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657434(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657434
Kim MAlghofaili RLi CYu L(2024)Dragon's Path: Synthesizing User-Centered Flying Creature Animation Paths for Outdoor Augmented Reality ExperiencesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657397(1-11)Online publication date: 13-Jul-2024
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Li LZhang XQian CWang RZhao M(2024)Autopilot Controller of Fixed-Wing Planes Based on Curriculum Reinforcement Learning Scheduled by Adaptive Learning CurveIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33603228:3(2182-2196)Online publication date: Jun-2024
https://doi.org/10.1109/TETCI.2024.3360322
Nan SQian C(2024)Reinforcement Learning Integrated Nonlinear Controller for Guaranteed Stability2024 19th Annual System of Systems Engineering Conference (SoSE)10.1109/SOSE62659.2024.10620953(172-177)Online publication date: 23-Jun-2024
https://doi.org/10.1109/SOSE62659.2024.10620953
Liu RZhang B(2024)Deep Reinforcement Learning of Physically Simulated Character ControlProceedings of 2024 Chinese Intelligent Systems Conference10.1007/978-981-97-8654-1_6(50-61)Online publication date: 27-Oct-2024
https://doi.org/10.1007/978-981-97-8654-1_6
Liu GWong S(2024)Mastering broom‐like tools for object transportation animation using deep reinforcement learningComputer Animation and Virtual Worlds10.1002/cav.225535:3Online publication date: 14-Jun-2024
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Reda DWon JYe Yvan de Panne MWinkler A(2023)Physics-based Motion Retargeting from Sparse InputsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36069286:3(1-19)Online publication date: 24-Aug-2023
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