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.

我们的目标是使机器人具备与人类类似的潜在触觉信号感知能力，通过使用一种名为 Imagine2touch 的方法，可以根据表示待触摸区域的视觉补丁来预测预期的触摸信号。我们使用了一种廉价而紧凑的触摸传感器 ReSkin 来收集所需的数据集，通过对五种基本几何形状和一种工具进行随机触摸。我们在其中两种形状上对 Imagine2touch 进行训练，并在 odd 工具上进行验证。我们通过将 Imagine2touch 应用于物体识别的下游任务来展示其有效性。在这个任务中，我们通过两个实验评估了 Imagine2touch 的性能，总共包括 5 个训练分布对象。在每个对象受到十次触摸后，Imagine2touch 的物体识别准确率达到了 58%，超过了一个本体感知基线。