Humans seemingly incorporate potential touch signals in their perception. Our goal is to equip robots with a similar capability, which we term Imagine2touch. Imagine2touch aims to predict the expected touch signal based on a visual patch representing the area to be touched. We use ReSkin, an inexpensive and compact touch sensor to collect the required dataset through random touching of five basic geometric shapes, and one tool. We train Imagine2touch on two out of those shapes and validate it on the ood. tool. We demonstrate the efficacy of Imagine2touch through its application to the downstream task of object recognition. In this task, we evaluate Imagine2touch performance in two experiments, together comprising 5 out of training distribution objects. Imagine2touch achieves an object recognition accuracy of 58% after ten touches per object, surpassing a proprioception baseline.

翻译：人类似乎能将潜在的触觉信号整合到感知过程中。我们的目标是赋予机器人类似的能力，即"Imagine2touch"。该方法旨在根据表示待接触区域的视觉图像，预测预期的触觉信号。我们采用廉价且紧凑的触觉传感器ReSkin，通过随机接触五种基础几何形状及一件工具来采集所需数据集。利用其中两种形状的数据训练Imagine2touch，并在分布外工具上验证其性能。我们通过将Imagine2touch应用于物体识别这一下游任务来证明其有效性。在该任务中，我们通过两项实验评估Imagine2touch的性能，共涉及五种训练集分布外的物体。Imagine2touch在每件物体接触十次后实现了58%的物体识别准确率，超越了基于本体感知的基线方法。