A pressure field model for fast, robust approximation of net contact force and moment between nominally rigid objects
Not on the map. Why?
Abstract (arXiv)
We introduce an approximate model for predicting the net contact wrench between nominally rigid objects for use in simulation, control, and state estimation. The model combines and generalizes two ideas: a bed of springs (an "elastic foundation") and hydrostatic pressure. In this model, continuous pressure fields are computed offline for the interior of each nominally rigid object. Unlike hydrostatics or elastic foundations, the pressure fields need not satisfy mechanical equilibrium conditions. When two objects nominally overlap, a contact surface is defined where the two pressure fields are equal. This static pressure is supplemented with a dissipative rate-dependent pressure and friction to determine tractions on the contact surface. The contact wrench between pairs of objects is an integral of traction contributions over this surface. The model evaluates much faster than elasticity-theory models, while showing the essential trends of force, moment, and stiffness increase with contact load. It yields continuous wrenches even for non-convex objects and coarse meshes. The method shows promise as sufficiently fast, accurate, and robust for design-in-simulation of robot controllers.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 1; 2025: 4; 2026: 1.
8 papers.
- 2022Do Differentiable Simulators Give Better Policy Gradients?cited by 83
- 2022Differentiable Physics Simulation of Dynamics-Augmented Neural Objectscited by 45
- 2025DyTact: Capturing Dynamic Contacts in Hand-Object Manipulationcited by 8
- 2025Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Controlcited by 5
- 2026HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learningcited by 3
- 2025Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematicscited by 1
- 2024RUMI: Rummaging Using Mutual Informationcited by 0
- 2025Bayesian Inverse Physics for Neuro-Symbolic Robot Learningcited by 0
- 2026HydroShear: Hydroelastic Shear Simulation for Tactile Sim-to-Real Reinforcement Learning28 Feb 2026
- 2025Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Control17 Jun 2025
- 2025Bayesian Inverse Physics for Neuro-Symbolic Robot Learning10 Jun 2025
- 2025DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation3 Jun 2025
- 2025Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematics28 Apr 2025
- 2024RUMI: Rummaging Using Mutual Information19 Aug 2024
- 2022Differentiable Physics Simulation of Dynamics-Augmented Neural Objects17 Oct 2022
- 2022Do Differentiable Simulators Give Better Policy Gradients?2 Feb 2022