Touch and Go: Learning from Human-Collected Vision and Touch

TL;DR

This paper introduces a large-scale, real-world dataset called Touch and Go that pairs visual and tactile data collected by humans, enabling new research in visuo-tactile learning and applications.

Contribution

It provides the first in-the-wild visuo-tactile dataset and demonstrates its usefulness in self-supervised learning, tactile-driven image stylization, and future tactile signal prediction.

Findings

Successful visuo-tactile feature learning

Effective tactile-driven image stylization

Accurate prediction of future tactile frames

Abstract

The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world. We propose a dataset with paired visual and tactile data called Touch and Go, in which human data collectors probe objects in natural environments using tactile sensors, while simultaneously recording egocentric video. In contrast to previous efforts, which have largely been confined to lab settings or simulated environments, our dataset spans a large number of "in the wild" objects and scenes. To demonstrate our dataset's effectiveness, we successfully apply it to a variety of tasks: 1) self-supervised visuo-tactile feature learning, 2) tactile-driven image stylization, i.e., making the visual appearance of an object more consistent with a given tactile signal, and 3) predicting future frames of a tactile signal from visuo-tactile inputs.