# ContactSDF: Signed Distance Functions as Multi-Contact Models for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2408.09612 · HTML: https://mlsanity.com/paper/2408.09612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2024.

## Built on by (most cited first, 1 of 1)

- [Ostrich: Taking Large Strides Through Stiff Contact in Differentiable Dynamics](https://mlsanity.com/paper/2609.08800/) (2026)

## Abstract (arXiv)

In this paper, we propose ContactSDF, a method that uses signed distance functions (SDFs) to approximate multi-contact models, including both collision detection and time-stepping routines. ContactSDF first establishes an SDF using the supporting plane representation of an object for collision detection, and then uses the generated contact dual cones to build a second SDF for time-stepping prediction of the next state. Those two SDFs create a differentiable and closed-form multi-contact dynamic model for state prediction, enabling efficient model learning and optimization for contact-rich manipulation. We perform extensive simulation experiments to show the effectiveness of ContactSDF for model learning and real-time control of dexterous manipulation. We further evaluate the ContactSDF on a hardware Allegro hand for on-palm reorientation tasks. Results show with around 2 minutes of learning on hardware, the ContactSDF achieves high-quality dexterous manipulation at a frequency of 30-60Hz. Project page https://yangwen-1102.github.io/contactsdf.github.io/