DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact

Abstract

Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional gradient information facilitates cloth-related applications. Our differentiable simulator extends a state-of-the-art cloth simulator based on Projective Dynamics (PD) and with dry frictional contact [Ly et al. 2020 ]. We draw inspiration from previous work [Du et al. 2021 ] to propose a fast and novel method for deriving gradients in PD-based cloth simulation with dry frictional contact. Furthermore, we conduct a comprehensive analysis and evaluation of the usefulness of gradients in contact-rich cloth simulation. Finally, we demonstrate the efficacy of our simulator in a number of downstream applications, including system identification, trajectory optimization for assisted dressing, closed-loop control, inverse design, and real-to-sim transfer. We observe a substantial speedup obtained from using our gradient information in solving most of these applications.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 03, 2022
Source ID
10.1145/3527660

Entities

People

  • Jie Xu
  • Kui Wu
  • Tao Du
  • Wojciech Matusik
  • Yifei Li

Organizations

  • Defense Advanced Research Projects Agency
  • Massachusetts Institute of Technology
  • Tencent

Tags

Fields of Study

  • Computer science

Readers

  • Materials Science
  • Operations Research
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy