Deep Learning for Tactile Understanding From Visual and Haptic Data

TL;DR

This paper presents a deep learning approach that combines visual and haptic data to classify surface properties, enabling robots to understand tactile features without physical contact and improving accuracy with combined signals.

Contribution

The paper introduces a unified deep learning model that integrates visual and physical interaction data for tactile understanding, surpassing hand-crafted feature methods.

Findings

Visual-only models can predict haptic properties effectively.

Combining visual and physical data improves classification accuracy.

Deep neural networks outperform traditional hand-designed features.

Abstract

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.