用视觉触觉传感器实现大范围3D力估计，跨设备通用性强。
FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors
- 基于多头Transformer，利用深度接触图像进行力回归
- 对未知物体误差仅4%，最大力达15N且泛化能力强
- 可适配多种触觉传感器，适合需要精确力反馈的机器人任务
本文针对基于视觉的触觉传感器估计3D接触力的问题，目标是在不同物体上实现高达15N的大范围力估计，并具备跨传感器泛化能力。为此，我们使用机械臂对多种压头在GelSight Mini传感器上施加压力，结合力传感器采集了超过20万次压痕数据，并训练了一个多头Transformer用于力回归。通过高精度数据采集与多目标优化，模型虽仅在简单形状和纹理上训练，却在未见真实物体数据集上达到4%的平均绝对误差。进一步评估显示该方法可推广至其他GelSight Mini和DIGIT传感器，并提出可复现的校准流程以适应新传感器。实际应用中，该方法成功用于称重和控制易损物体形变等任务，依赖于精确的力反馈。
原文摘要 · Abstract (English)
In this paper, we tackle the problem of estimating 3D contact forces using vision-based tactile sensors. In particular, our goal is to estimate contact forces over a large range (up to 15 N) on any objects while generalizing across different vision-based tactile sensors. Thus, we collected a dataset of over 200K indentations using a robotic arm that pressed various indenters onto a GelSight Mini sensor mounted on a force sensor and then used the data to train a multi-head transformer for force regression. Strong generalization is achieved via accurate data collection and multi-objective optimization that leverages depth contact images. Despite being trained only on primitive shapes and textures, the regressor achieves a mean absolute error of 4\% on a dataset of unseen real-world objects. We further evaluate our approach's generalization capability to other GelSight mini and DIGIT sensors, and propose a reproducible calibration procedure for adapting the pre-trained model to other vision-based sensors. Furthermore, the method was evaluated on real-world tasks, including weighing objects and controlling the deformation of delicate objects, which relies on accurate force feedback. Project webpage: http://prg.cs.umd.edu/FeelAnyForce
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