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DiffAqua: a differentiable computational design pipeline for soft underwater swimmers with shape interpolation

Authors:

Andrew Spielberg,

Robert K. Katzschmann,

Wojciech MatusikAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 40, Issue 4

Article No.: 132, Pages 1 - 14

https://doi.org/10.1145/3450626.3459832

Published: 19 July 2021 Publication History

Abstract

The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipeline for co-designing a soft swimmer's geometry and controller. Our pipeline unlocks gradient-based algorithms for discovering novel swimmer designs more efficiently than traditional gradient-free solutions. We propose Wasserstein barycenters as a basis for the geometric design of soft underwater swimmers since it is differentiable and can naturally interpolate between bio-inspired base shapes via optimal transport. By combining this design space with differentiable simulation and control, we can efficiently optimize a soft underwater swimmer's performance with fewer simulations than baseline methods. We demonstrate the efficacy of our method on various design problems such as fast, stable, and energy-efficient swimming and demonstrate applicability to multi-objective design.

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Index Terms

DiffAqua: a differentiable computational design pipeline for soft underwater swimmers with shape interpolation
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Physical simulation
    2. Shape modeling
      1. Volumetric models
  2. Modeling and simulation

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 40, Issue 4

August 2021

2170 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3450626

Editor:
Sylvain Paris
Adobe Inc.

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Published: 19 July 2021

Published in TOG Volume 40, Issue 4

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https://dl.acm.org/doi/10.1145/3687978
Le QBu JQu YZhu BDu T(2024)Computational Biomimetics of Winged SeedsACM Transactions on Graphics10.1145/368789943:6(1-13)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687899
Zhan XFu RRitchie D(2024)CharacterMixer: Rig‐Aware Interpolation of 3D CharactersComputer Graphics Forum10.1111/cgf.1504743:2Online publication date: 30-Apr-2024
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Cao GPeng Z(2024)RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D EnvironmentsIEEE Journal on Multiscale and Multiphysics Computational Techniques10.1109/JMMCT.2024.34643739(330-340)Online publication date: 2024
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Li MMatthews DKriegman S(2024)Reinforcement learning for freeform robot design2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610048(8799-8806)Online publication date: 13-May-2024
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Yasa OToshimitsu YMichelis MJones LFilippi MBuchner TKatzschmann R(2023)An Overview of Soft RoboticsAnnual Review of Control, Robotics, and Autonomous Systems10.1146/annurev-control-062322-1006076:1(1-29)Online publication date: 3-May-2023
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