Robots operating in the real world must plan through environments that deform, yield, and reconfigure under contact, requiring interaction-aware 3D representations that extend beyond static geometric occupancy. To address this, we introduce neural tactile fields, a novel 3D representation that maps spatial locations to the expected tactile response upon contact. Our model predicts these neural tactile fields from a single monocular RGB image -- the first method to do so. When integrated with off-the-shelf path planners, neural tactile fields enable robots to generate paths that avoid high-resistance objects while deliberately routing through low-resistance regions (e.g. foliage), rather than treating all occupied space as equally impassable. Empirically, our learning framework improves volumetric 3D reconstruction by $85.8\%$ and surface reconstruction by $26.7\%$ compared to state-of-the-art monocular 3D reconstruction methods (LRM and Direct3D).

翻译：在现实世界中运行的机器人必须规划通过那些在接触下会发生形变、屈服和重构的环境，这需要超越静态几何占据度的交互感知三维表征。为此，我们提出了神经触觉场，这是一种新颖的三维表征方法，它将空间位置映射到接触时预期的触觉响应。我们的模型能够从单张单目RGB图像预测这些神经触觉场——这是首个实现此功能的方法。当与现成的路径规划器结合使用时，神经触觉场使机器人能够生成避开高阻力物体、同时有意识地规划通过低阻力区域（例如植被）的路径，而不是将所有被占据的空间视为同等不可通行。实验表明，与最先进的单目三维重建方法（LRM和Direct3D）相比，我们的学习框架将体三维重建精度提升了$85.8\%$，表面重建精度提升了$26.7\%$。