High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: \textit{tactile saliency} for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision. In analogy to visual saliency, this concept involves identifying key information in tactile images captured by a tactile sensor. While visual saliency datasets are commonly annotated by humans, manually labelling tactile images is challenging due to their counterintuitive patterns. To address this challenge, we propose a novel approach comprised of three interrelated networks: 1) a Contact Depth Network (ConDepNet), which generates a contact depth map to localize deformation in a real tactile image that contains target and noise features; 2) a Tactile Saliency Network (TacSalNet), which predicts a tactile saliency map to describe the target areas for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen), which generates noise features to train the TacSalNet. Experimental results in contact pose estimation and edge-following in the presence of distractors showcase the accurate prediction of target features from real tactile images. Overall, our tactile saliency prediction approach gives robust sim-to-real tactile control in environments with unknown distractors. Project page: https://sites.google.com/view/tactile-saliency/.

翻译：高分辨率触觉感知能在接触密集型机器人任务中提供关于局部接触的精确信息。然而，此类任务在非结构化环境中的部署仍有待深入研究。为提升触觉机器人在非结构化环境中的鲁棒性，我们受神经科学中人类触觉注意力机制与计算机视觉中视觉显著性预测问题的启发，提出并研究了一个新概念：机器人触觉的“触觉显著性”。与视觉显著性类似，该概念涉及识别触觉传感器捕捉到的触觉图像中的关键信息。鉴于视觉显著性数据集通常由人工标注，而触觉图像因具有反直觉模式而难以手动标注，我们提出一种包含三个相互关联网络的新方法：1）接触深度网络（ConDepNet），生成接触深度图以定位包含目标特征与噪声特征的真实触觉图像中的形变区域；2）触觉显著性网络（TacSalNet），根据输入的接触深度图预测触觉显著性图以描述目标区域；3）触觉噪声生成器（TacNGen），生成噪声特征用于训练TacSalNet。在有干扰物的接触位姿估计与边缘跟踪实验中，结果展示了从真实触觉图像中准确预测目标特征的能力。总体而言，我们的触觉显著性预测方法在存在未知干扰物的环境中实现了鲁棒的仿真到现实触觉控制。项目页面：https://sites.google.com/view/tactile-saliency/。