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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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Cited By

Agarwal AWilson AMan TAdelson EGkioulekas IYuan W(2025)Vision-based tactile sensor design using physically based renderingCommunications Engineering10.1038/s44172-025-00350-44:1Online publication date: 14-Feb-2025
https://doi.org/10.1038/s44172-025-00350-4
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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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Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 19 July 2021

Published in TOG Volume 40, Issue 4

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Cited By

Agarwal AWilson AMan TAdelson EGkioulekas IYuan W(2025)Vision-based tactile sensor design using physically based renderingCommunications Engineering10.1038/s44172-025-00350-44:1Online publication date: 14-Feb-2025
https://doi.org/10.1038/s44172-025-00350-4
Chen MWang YHu DZhu PGuo JGuo Y(2024)DTDMat: A Comprehensive SVBRDF Dataset with Detailed Text DescriptionsProceedings of the 19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry10.1145/3703619.3706053(1-15)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1145/3703619.3706053
Wang LZhang LGao FKang YZhang J(2024)NFPLight: Deep SVBRDF Estimation via the Combination of Near and Far Field Point LightingACM Transactions on Graphics10.1145/368797843:6(1-11)Online publication date: 19-Dec-2024
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
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Zhan XFu RRitchie D(2024)CharacterMixer: Rig‐Aware Interpolation of 3D CharactersComputer Graphics Forum10.1111/cgf.1504743:2Online publication date: 30-Apr-2024
https://doi.org/10.1111/cgf.15047
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Xie YPinskier JLiow LHoward DIida F(2024)A ’MAP’ to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS58592.2024.10802427(11408-11415)Online publication date: 14-Oct-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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Alaa El-Din KForte AKasim MMiniati FVinko S(2024)STEP: extraction of underlying physics with robust machine learningRoyal Society Open Science10.1098/rsos.23137411:6Online publication date: 5-Jun-2024
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