用扩散模型精确定位机器人多接触点，精度达0.44cm。
CDM: Contact Diffusion Model for Multi-Contact Point Localization
- 基于扩散模型，结合历史输出预测多接触点位置。
- 真实场景下单接触误差0.44cm，双接触误差1.24cm。
- 适合需要高精度触觉定位的机器人应用。
本文提出一种接触扩散模型（CDM），用于解决机器人多接触点定位问题。该方法利用安装在关节扭矩传感器和基座力/扭矩传感器上的设备，通过扩散模型克服多个接触点与受力组合产生相同传感器读数的奇异性。CDM采用条件生成方式，依赖先前输出以捕捉多接触场景的时间依赖性；同时在去噪过程中引入符号距离场，有效处理机器人表面复杂形状。实验表明，该方法可在仿真和真实世界中实现高精度定位，计算耗时仅15.97ms，真实场景下单接触误差为0.44cm，双接触误差为1.24cm。
原文摘要 · Abstract (English)
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.
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