To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer.

翻译：为实现与人类相媲美的灵巧操作能力，机器人必须智能处理触觉传感器数据。基于触觉像素（taxel）的触觉信号通常具有较低的空间分辨率，且缺乏标准化表示。本文提出一种新颖的框架HyperTaxel，通过学习触觉像素信号的几何感知表示，以解决其空间分辨率相关的挑战。我们利用该表示及对比学习目标，将稀疏的低分辨率触觉像素信号编码并映射至高分辨率接触表面。为处理此类信号固有的不确定性，我们通过联合概率分布建模多个同步接触点以提升触觉像素的超分辨率性能。通过与两种基线方法进行比较来评估所提表示，实验结果表明我们的表示优于基线方法。此外，定性分析显示所学表示能够捕捉接触表面的几何特征（如平坦度、曲率和边缘），并能在不同物体与传感器配置间实现泛化。进一步实验表明，该表示能有效提升多种下游任务的性能，包括表面分类、六自由度手内姿态估计以及仿真到现实的迁移。