Tactile sensing has become a popular sensing modality for robot manipulators, due to the promise of providing robots with the ability to measure the rich contact information that gets transmitted through its sense of touch. Among the diverse range of information accessible from tactile sensors, torques transmitted from the grasped object to the fingers through extrinsic environmental contact may be particularly important for tasks such as object insertion. However, tactile torque estimation has received relatively little attention when compared to other sensing modalities, such as force, texture, or slip identification. In this work, we introduce the notion of the Tactile Dipole Moment, which we use to estimate tilt torques from gel-based visuotactile sensors. This method does not rely on deep learning, sensor-specific mechanical, or optical modeling, and instead takes inspiration from electromechanics to analyze the vector field produced from 2D marker displacements. Despite the simplicity of our technique, we demonstrate its ability to provide accurate torque readings over two different tactile sensors and three object geometries, and highlight its practicality for the task of USB stick insertion with a compliant robot arm. These results suggest that simple analytical calculations based on dipole moments can sufficiently extract physical quantities from visuotactile sensors.

翻译：触觉传感已成为机器人操作器的一种流行感知模态，因其有望使机器人能够通过触觉传递丰富的接触信息。在触觉传感器可获取的多种信息中，通过外部环境接触从被抓取物体传递到手指的扭矩，对于物体插入等任务尤为关键。然而，与力、纹理或滑移识别等其他感知模态相比，触觉扭矩估计受到的关注相对较少。本文提出“触觉偶极矩”概念，用于从基于凝胶的视觉触觉传感器中估计倾斜扭矩。该方法不依赖深度学习、传感器特定的机械或光学建模，而是受机电学启发，分析二维标记位移产生的矢量场。尽管技术简单，我们展示了其能够在两种不同触觉传感器和三种物体几何形状上提供准确的扭矩读数，并强调了其在具有柔性机器人臂的USB插入任务中的实用性。这些结果表明，基于偶极矩的简单解析计算足以从视觉触觉传感器中提取物理量。