CDM: Contact Diffusion Model for Multi-Contact Point Localization

TL;DR

This paper introduces CDM, a diffusion-based learning model that accurately localizes multiple contact points on robots using sensor data, addressing challenges like contact ambiguity and complex surface shapes.

Contribution

The paper presents a novel diffusion model for multi-contact localization that incorporates time-dependent conditioning and signed distance fields for high accuracy.

Findings

Achieves 0.44cm error in single-contact localization

Operates at 15.97ms in real-time

Effective in both simulation and real-world tests

Abstract

In this paper, we propose a Contact Diffusion Model (CDM), a novel learning-based approach for multi-contact point localization. We consider a robot equipped with joint torque sensors and a force/torque sensor at the base. By leveraging a diffusion model, CDM addresses the singularity where multiple pairs of contact points and forces produce identical sensor measurements. We formulate CDM to be conditioned on past model outputs to account for the time-dependent characteristics of the multi-contact scenarios. Moreover, to effectively address the complex shape of the robot surfaces, we incorporate the signed distance field in the denoising process. Consequently, CDM can localize contacts at arbitrary locations with high accuracy. Simulation and real-world experiments demonstrate the effectiveness of the proposed method. In particular, CDM operates at 15.97ms and, in the real world, achieves an error of 0.44cm in single-contact scenarios and 1.24cm in dual-contact scenarios.