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AdaptNet: Policy Adaptation for Physics-Based Character Control

Authors:

Sheldon Andrews,

Morgan Mcguire,

Ioannis Karamouzas,

Victor ZordanAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 6

Article No.: 177, Pages 1 - 17

https://doi.org/10.1145/3618375

Published: 05 December 2023 Publication History

Abstract

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available at https://motion-lab.github.io/AdaptNet.

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Cited By

Yu JZheng RWang Y(2025)Stability Analysis of Aged Locomotion in Physically Simulated Virtual Environments2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR63409.2025.00045(238-243)Online publication date: 27-Jan-2025
https://doi.org/10.1109/AIxVR63409.2025.00045
Peng QXu HWang YLiu HHuo CWang W(2024)PEPT: Expert Finding Meets Personalized Pre-TrainingACM Transactions on Information Systems10.1145/369038043:1(1-26)Online publication date: 26-Nov-2024
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Index Terms

AdaptNet: Policy Adaptation for Physics-Based Character Control
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 6

December 2023

1565 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3632123

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Copyright © 2023 Owner/Author.

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Published: 05 December 2023

Published in TOG Volume 42, Issue 6

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Yu JZheng RWang Y(2025)Stability Analysis of Aged Locomotion in Physically Simulated Virtual Environments2025 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR63409.2025.00045(238-243)Online publication date: 27-Jan-2025
https://doi.org/10.1109/AIxVR63409.2025.00045
Peng QXu HWang YLiu HHuo CWang W(2024)PEPT: Expert Finding Meets Personalized Pre-TrainingACM Transactions on Information Systems10.1145/369038043:1(1-26)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3690380
Huang GFang QZhang ZLiu LFu X(2024)Stochastic Normal Orientation for Point CloudsACM Transactions on Graphics10.1145/368794443:6(1-12)Online publication date: 19-Nov-2024
https://dl.acm.org/doi/10.1145/3687944
Wu YDou ZIshiwaka YOgawa SLou YWang WLiu LKomura T(2024)CBIL: Collective Behavior Imitation Learning for Fish from Real VideosACM Transactions on Graphics10.1145/368790443:6(1-17)Online publication date: 19-Nov-2024
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