论文标题
教摄像机感觉：从图像中估算表面的触觉物理特性
Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
论文作者
论文摘要
视觉输入和触觉传感之间的连接对于对象操纵任务（例如抓紧和推动）至关重要。在这项工作中，我们介绍了从视觉信息中估算一组触觉物理特性的挑战性任务。我们旨在建立一个模型，以了解视觉信息和触觉物理属性之间的复杂映射。我们构建具有超过400个多视图像序列和相应触觉属性的第一个类似图像的数据集。跨类别的总共有15种触觉物理特性，包括摩擦，合规性，粘附，纹理和导热率，然后由我们的模型估算。我们开发了一个跨模式框架，该框架由一个对抗性目标和新型的视觉侵蚀性关节分类损失组成。此外，我们开发了一个神经体系结构搜索框架，能够选择视角的最佳组合来估计给定的物理属性。
The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.
