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

Published: 17 Sept 2022, Last Modified: 22 Oct 2023NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
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. We successfully apply our dataset to a variety of multimodal learning 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.
Author Statement: Yes
TL;DR: We introduce “Touch and Go”, a human-collected dataset containing paired visual and tactile data from real-world scenes.
Supplementary Material: pdf
URL: https://touch-and-go.github.io/
License: CC BY 4.0
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Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2211.12498/code)
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