Touch and Go: Learning from Human-Collected Vision and Touch
This provides a dataset for robotics and AI researchers to improve object interaction in real-world settings, though it is incremental as it builds on prior lab-based or simulated efforts.
The authors tackled the problem of associating touch with sight for physical interaction tasks by introducing the Touch and Go dataset, which includes paired visual and tactile data collected in natural environments, and demonstrated its effectiveness on tasks like self-supervised feature learning, tactile-driven image stylization, and tactile signal prediction.
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.