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Scalable Differentiable Physics for Learning and Control

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun, Ming C. Lin

TL;DR

This document provides ICML 2020 submission and formatting guidelines, detailing electronic PDF submissions, page limits, anonymization, and camera-ready requirements. It specifies precise formatting for fonts, margins, figures, tables, and references, as well as citation conventions and self-citation rules during blind review. The guidelines ensure consistency across submissions through structured sectioning, captions, and layout constraints. Proper adherence facilitates fair review and smooth publication workflow.

Abstract

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions. To accommodate objects with arbitrary geometry and topology, we adopt meshes as our representation and leverage the sparsity of contacts for scalable differentiable collision handling. Collisions are resolved in localized regions to minimize the number of optimization variables even when the number of simulated objects is high. We further accelerate implicit differentiation of optimization with nonlinear constraints. Experiments demonstrate that the presented framework requires up to two orders of magnitude less memory and computation in comparison to recent particle-based methods. We further validate the approach on inverse problems and control scenarios, where it outperforms derivative-free and model-free baselines by at least an order of magnitude.

Scalable Differentiable Physics for Learning and Control

TL;DR

This document provides ICML 2020 submission and formatting guidelines, detailing electronic PDF submissions, page limits, anonymization, and camera-ready requirements. It specifies precise formatting for fonts, margins, figures, tables, and references, as well as citation conventions and self-citation rules during blind review. The guidelines ensure consistency across submissions through structured sectioning, captions, and layout constraints. Proper adherence facilitates fair review and smooth publication workflow.

Abstract

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilities of differentiable physics solvers remain limited. We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions. To accommodate objects with arbitrary geometry and topology, we adopt meshes as our representation and leverage the sparsity of contacts for scalable differentiable collision handling. Collisions are resolved in localized regions to minimize the number of optimization variables even when the number of simulated objects is high. We further accelerate implicit differentiation of optimization with nonlinear constraints. Experiments demonstrate that the presented framework requires up to two orders of magnitude less memory and computation in comparison to recent particle-based methods. We further validate the approach on inverse problems and control scenarios, where it outperforms derivative-free and model-free baselines by at least an order of magnitude.

Paper Structure

This paper contains 18 sections, 1 equation, 1 figure, 1 table, 1 algorithm.

Table of Contents

  1. Electronic Submission
  2. Submitting Papers
  3. Submitting Final Camera-Ready Copy
  4. Format of the Paper
  5. Dimensions
  6. Title
  7. Author Information for Submission
  8. Self-Citations
  9. Camera-Ready Author Information
  10. Abstract
  11. Partitioning the Text
  12. Sections and Subsections
  13. Paragraphs and Footnotes
  14. Figures
  15. Algorithms
  16. ...and 3 more sections

Figures (1)

  • Figure 1: Historical locations and number of accepted papers for International Machine Learning Conferences (ICML 1993 -- ICML 2008) and International Workshops on Machine Learning (ML 1988 -- ML 1992). At the time this figure was produced, the number of accepted papers for ICML 2008 was unknown and instead estimated.