Robots which interact with the physical world will benefit from a
fine-grained tactile understanding of objects and surfaces. Additionally, for
certain tasks, robots may need to know the haptic properties of an object
before touching it. To enable better tactile understanding for robots, we
propose a method of classifying surfaces with haptic adjectives (e.g.,
compressible or smooth) from both visual and physical interaction data. Humans
typically combine visual predictions and feedback from physical interactions to
accurately predict haptic properties and interact with the world. Inspired by
this cognitive pattern, we propose and explore a purely visual haptic
prediction model. Purely visual models enable a robot to "feel" without
physical interaction. Furthermore, we demonstrate that using both visual and
physical interaction signals together yields more accurate haptic
classification. Our models take advantage of recent advances in deep neural
networks by employing a unified approach to learning features for physical
interaction and visual observations. Even though we employ little domain
specific knowledge, our model still achieves better results than methods based
on hand-designed features.

本文提出了一种方法，通过视觉和物理交互数据分类表面，以使机器人更好地了解物体的触觉特性；通过统一学习物理交互和视觉观察特征的方法，使用深度神经网络模型实现更准确的触觉分类。