TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning

Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, but visual observations alone do not directly measure contact and force during physical interaction. Recent visual-tactile datasets have begun to fill this gap, but the available volume of synchronized tactile data remains far smaller than that of human video, and the largest existing resources often combine data collected with different sensors or annotation procedures. Datasets containing hundreds of hours of human vision and touch captured through one consistent sensing pipeline remain scarce. We introduce TouchScale, a 500-hour dataset of contact-rich human interaction collected through a consistent sensing pipeline, comprising approximately 2K predefined task descriptions spanning everyday activities and structured manipulation. Each recording contains time-aligned egocentric RGB-D, bilateral wrist RGB and tactile measurements. At this scale, we ask a simple question: what does scale unlock for visual-tactile learning? We investigate this via cross-dataset generalization, transferable representation learning, and robotic manipulation. Using the same vision-to-touch architecture, training on TouchScale instead of EgoTouch improves zero-shot cIoU on EgoTactile by 35.1%. Pretraining on TouchScale improves downstream action recognition accuracy by 49.3% compared with other visual-tactile pretraining datasets. Using TouchScale for visual-tactile pretraining improves contact-rich manipulation success rate from 40% to 70%. Together, our scalable collection paradigm, large-scale dataset, and study establish a foundation for human visual-tactile learning and its transfer to robotic manipulation.