Novel Algorithms for Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Construction

• URL: http://arxiv.org/abs/2604.17538v1
• Date: Sun, 19 Apr 2026 16:58:09 GMT
• Title: Novel Algorithms for Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Construction
• Abstract summary: Developing methods that make use of first/second order information about the dynamics holds great promise.<n>Main bottleneck in this research direction is the difficulty in obtaining useful gradients and Hessians.<n>This abstract proposes a method that can address the collision detection part of the puzzle in a manner that is smoothly differentiable and massively vectorizable.
• Score: 26.543084981943778
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. Developing methods that make use of first/second order information about the dynamics holds great promise in terms of increasing the solution speed and computational efficiency. The main bottleneck in this research direction is the difficulty in obtaining useful gradients and Hessians, due to pathologies in all three steps of a common simulation pipeline: i) collision detection, ii) contact dynamics, iii) time integration. This abstract proposes a method that can address the collision detection part of the puzzle in a manner that is smoothly differentiable and massively vectorizable. This is achieved via two contributions: i) a highly expressive class of analytical SDF primitives that can efficiently represent complex 3D surfaces, ii) a novel contact manifold generation routine that makes use of this geometry representation.

Related papers

• Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries [51.028432812178266]
  Transolver-3 is a new member of the Transolver family designed for high-fidelity physics simulations.<n>We show that Transolver-3 is capable of handling meshes with over 160 million cells, achieving impressive performance across three challenging simulation benchmarks.
  arXiv  Detail & Related papers  (2026-02-04T16:52:44Z)
• COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimation [58.47973015036709]
  3D pose estimation from sparse multi-views is a critical task for action recognition, sports analysis, and human-robot interaction.<n>We propose COMPOSE, a novel framework that formulates multi-view pose correspondence matching as a hypergraph problem.<n> COMPOSE achieves improvements of up to 23% in average precision over previous optimization-based methods and up to 11% over self-supervised end-to-end learned methods.
  arXiv  Detail & Related papers  (2026-01-14T18:50:17Z)
• Lightning Grasp: High Performance Procedural Grasp Synthesis with Contact Fields [55.014365168982884]
  We present Lightning Grasp, a novel high-performance procedural grasp synthesis algorithm.<n>It achieves orders-of-magnitude speedups over state-of-the-art approaches.<n>We open-source our system to propel further innovation in robotic manipulation.
  arXiv  Detail & Related papers  (2025-11-10T18:59:44Z)
• Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline [64.42938561167402]
  We propose an online 3D reconstruction method using 3D Gaussian-based SLAM, combined with a feed-forward recurrent prediction module.<n>This approach replaces slow test-time optimization with fast network inference, significantly improving tracking speed.<n>Our method achieves performance on par with the state-of-the-art SplaTAM, while reducing tracking time by more than 90%.
  arXiv  Detail & Related papers  (2025-08-06T16:16:58Z)
• Learning Localization of Body and Finger Animation Skeleton Joints on Three-Dimensional Models of Human Bodies [0.0]
  Our work proposes one such solution to the problem of positioning body and finger animation skeleton joints within 3D models of human bodies. By comparing our method with the state-of-the-art, we show that it is possible to achieve significantly better results with a simpler architecture.
  arXiv  Detail & Related papers  (2024-07-11T13:16:02Z)
• Differentiable DG with Neural Operator Source Term Correction [0.0]
  We introduce an end-to-end differentiable framework for solving the compressible Navier-Stokes equations.<n>This integrated approach combines a differentiable discontinuous Galerkin solver with a neural network source term.<n>We demonstrate the performance of the proposed framework through two examples.
  arXiv  Detail & Related papers  (2023-10-29T04:26:23Z)
• Learned Indexing in Proteins: Substituting Complex Distance Calculations with Embedding and Clustering Techniques [2.239917051803691]
  The authors present a lightweight solution for the complex problem of 3D protein structure search. The solution consists of three steps -- (i) transformation of 3D protein structural information into very compact vectors, (ii) use of a probabilistic model to group these vectors and respond to queries by returning a given number of similar objects, and (iii) a final filtering step which applies basic vector distance functions to refine the result.
  arXiv  Detail & Related papers  (2022-08-18T15:22:42Z)
• Efficient Differentiable Simulation of Articulated Bodies [89.64118042429287]
  We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks. We show that reinforcement learning with articulated systems can be accelerated using gradients provided by our method.
  arXiv  Detail & Related papers  (2021-09-16T04:48:13Z)
• DiffPD: Differentiable Projective Dynamics with Contact [65.88720481593118]
  We present DiffPD, an efficient differentiable soft-body simulator with implicit time integration. We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton's method in various applications.
  arXiv  Detail & Related papers  (2021-01-15T00:13:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.