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:
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在未知干扰环境中，通过提出触觉显著性预测方法，可以提高机器人对触觉的鲁棒控制，并精确预测真实触觉图像中的目标特征。