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.
@article{arxiv.2502.06109,
title = {CDM: Contact Diffusion Model for Multi-Contact Point Localization},
author = {Seo Wook Han and Min Jun Kim},
journal= {arXiv preprint arXiv:2502.06109},
year = {2025}
}
Comments
This paper has been accepted for publication at the 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, USA