Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on dexterous hands, poses significant challenges for effective tactile feature learning, especially for 3D tactile data, as there are no large standardized datasets and no strong pretrained backbones. To address these challenges, we propose a novel canonical representation that reduces the difficulty of 3D tactile feature learning and further introduces a force-based self-supervised pretraining task to capture both local and net force features, which are crucial for dexterous manipulation. Our method achieves an average success rate of 78% across four fine-grained, contact-rich dexterous manipulation tasks in real-world experiments, demonstrating effectiveness and robustness compared to other methods. Further analysis shows that our method fully utilizes both spatial and force information from 3D tactile data to accomplish the tasks. The videos can be viewed at https://3dtacdex.github.io.

翻译：触觉感知在使机器人执行精细、接触密集的任务中发挥着至关重要的作用。然而，由于在灵巧手上的覆盖范围大，触觉数据的高维度给有效的触觉特征学习带来了重大挑战，特别是对于三维触觉数据，因为缺乏大型标准化数据集和强大的预训练骨干网络。为应对这些挑战，我们提出了一种新颖的规范表示，以降低三维触觉特征学习的难度，并进一步引入了一种基于力的自监督预训练任务，以捕获对灵巧操作至关重要的局部力和净力特征。我们的方法在真实世界实验中的四个精细、接触密集的灵巧操作任务上实现了平均78%的成功率，与其他方法相比，证明了其有效性和鲁棒性。进一步的分析表明，我们的方法充分利用了来自三维触觉数据的空间和力信息来完成这些任务。视频可在 https://3dtacdex.github.io 查看。