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：高分辨率触觉传感可在接触密集型机器人任务中提供精确的局部接触信息。然而，此类任务在非结构化环境中的部署仍鲜有研究。为提升非结构化环境下触觉机器人控制的鲁棒性，受神经科学中人类触觉注意力机制及计算机视觉中视觉显著性预测问题的启发，我们提出并研究了一个新概念：机器人触觉中的**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获的触觉图像中的关键信息。尽管视觉显著性数据集通常由人工标注，但触觉图像因其反直觉的模式而难以手动标记。为应对这一挑战，我们提出了一种包含三个相互关联网络的新方法：1）接触深度网络（ConDepNet），用于生成接触深度图以定位包含目标特征和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），用于根据输入接触深度图预测描述目标区域的触觉显著性图；3）触觉噪声生成器（TacNGen），用于生成噪声特征以训练TacSalNet。在存在干扰物的接触位姿估计和边缘跟踪实验结果表明，该方法能够准确预测真实触觉图像中的目标特征。总体而言，我们的触觉显著性预测方法可在包含未知干扰物的环境中实现鲁棒的仿真到实际触觉控制。项目页面：https://sites.google.com/view/tactile-saliency/