用触觉信号生成物体3D模型，提升机器人感知精度与可靠性
Tactile Neural De-rendering
- 通过生成模型从触觉数据重建物体局部3D结构
- 实现高精度姿态估计并量化感知不确定性
- 适合触觉感知、机器人抓取等场景使用
触觉传感在视觉受限的机器人感知中具有重要价值。传统触觉姿态估计方法依赖复杂的传感器建模或接触区域估计，过程繁琐且结果确定性过强。本文提出触觉神经去渲染（Tactile Neural De-rendering），利用生成模型仅根据触觉信号重建物体的局部3D表示。通过将物体视为嵌入指尖的虚拟相机所观测的场景，该方法提供更直观灵活的触觉表征。此3D重建不仅支持精确的姿态估计，还能量化不确定性，为机器人触觉感知提供稳健框架。
原文摘要 · Abstract (English)
Tactile sensing has proven to be an invaluable tool for enhancing robotic perception, particularly in scenarios where visual data is limited or unavailable. However, traditional methods for pose estimation using tactile data often rely on intricate modeling of sensor mechanics or estimation of contact patches, which can be cumbersome and inherently deterministic. In this work, we introduce Tactile Neural De-rendering, a novel approach that leverages a generative model to reconstruct a local 3D representation of an object based solely on its tactile signature. By rendering the object as though perceived by a virtual camera embedded at the fingertip, our method provides a more intuitive and flexible representation of the tactile data. This 3D reconstruction not only facilitates precise pose estimation but also allows for the quantification of uncertainty, providing a robust framework for tactile-based perception in robotics.
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