Tactile sensing is vital for human dexterous manipulation, however, it has not been widely used in robotics. Compact, low-cost sensing platforms can facilitate a change, but unlike their popular optical counterparts, they are difficult to deploy in high-fidelity tasks due to their low signal dimensionality and lack of a simulation model. To overcome these challenges, we introduce PseudoTouch which links high-dimensional structural information to low-dimensional sensor signals. It does so by learning a low-dimensional visual-tactile embedding, wherein we encode a depth patch from which we decode the tactile signal. We collect and train PseudoTouch on a dataset comprising aligned tactile and visual data pairs obtained through random touching of eight basic geometric shapes. We demonstrate the utility of our trained PseudoTouch model in two downstream tasks: object recognition and grasp stability prediction. In the object recognition task, we evaluate the learned embedding's performance on a set of five basic geometric shapes and five household objects. Using PseudoTouch, we achieve an object recognition accuracy 84% after just ten touches, surpassing a proprioception baseline. For the grasp stability task, we use ACRONYM labels to train and evaluate a grasp success predictor using PseudoTouch's predictions derived from virtual depth information. Our approach yields a 32% absolute improvement in accuracy compared to the baseline relying on partial point cloud data. We make the data, code, and trained models publicly available at https://pseudotouch.cs.uni-freiburg.de.

翻译：触觉感知对于人类灵巧操作至关重要，然而在机器人领域尚未得到广泛应用。紧凑、低成本的传感平台可推动这一变革，但与广泛采用的光学传感器不同，由于信号维度低且缺乏仿真模型，此类触觉传感器难以部署于高保真任务中。为克服这些挑战，我们提出PseudoTouch，将高维结构信息与低维传感器信号相连接。该方法通过学习低维视觉-触觉嵌入实现，其中我们对深度图像块进行编码，并由此解码触觉信号。我们通过随机触摸八种基本几何形状收集对齐的触觉-视觉数据对，并基于此数据集训练PseudoTouch。我们在两个下游任务中验证训练后PseudoTouch模型的实用性：物体识别与抓取稳定性预测。在物体识别任务中，我们在五种基本几何形状和五种家居物体组成的数据集上评估所学嵌入的性能。使用PseudoTouch仅需十次触摸即可达到84%的物体识别准确率，超越本体感知基线方法。在抓取稳定性任务中，我们利用ACRONYM标注数据，基于PseudoTouch从虚拟深度信息生成的预测结果来训练和评估抓取成功率预测器。相比依赖局部点云数据的基线方法，我们的方法在准确率上实现了32%的绝对提升。我们将数据、代码及训练模型公开于https://pseudotouch.cs.uni-freiburg.de。