arXiv Computer Vision
EgoTac: In-the-wild Tactile Prediction from Egocentric Vision
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TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning
arXiv:2610.10288v1 Announce Type: new Abstract: Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the...
arXiv AI
Sep 18
TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces from video. It uses 500 hours of pressure‑glove recordings and hand‑object interaction data, and introduces TwinTouch‑20H, a dataset of 20 hours of paired visual data where generative models render gloved recordings as bare‑hand observations while preserving tactile labels. The system outperforms prior methods on OakInk2, generalizes qualitatively to natural bare‑hand egocentric videos from unseen datasets, and improves consistently as glove supervision scales.
Hugging Face Trending Papers
Sep 17
TouchSight: Bare-Handed Tactile Prediction from Egocentric Video via Generative Visual Augmentation
TouchSight is a monocular egocentric vision framework that predicts dense full-hand contact forces without tactile sensors. It uses 500 hours of pressure-glove data and a 20-hour TwinTouch-20H dataset where generative models render gloved recordings as bare-hand videos, bridging the appearance gap. The system outperforms previous methods on OakInk2, generalizes to unseen natural bare-hand egocentric videos, and improves as glove supervision increases.
arXiv AI
Jun 9
EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video
arXiv:2606. 09243v1 Announce Type: cross Abstract: Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware.
arXiv Computer Vision
Oct 28
Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation
DreamTacVLA is a Vision‑Language‑Action framework that incorporates high‑resolution tactile images alongside wrist‑camera and third‑person vision to enable robots to anticipate and react to contact dynamics. It uses a Hierarchical Spatial Alignment loss to align tactile tokens with visual counterparts and a tactile world model to predict future tactile signals, trained on a hybrid dataset from digital twins and real experiments. The approach achieves up to 95% success on contact‑rich manipulation tasks, outperforming existing VLA baselines.
arXiv Computer Vision
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Touch-JEPA: Tactile Representation Learning from Hand-Object Video with Limited Tactile Supervision
Touch-JEPA is a framework that learns tactile representations from hand‑object videos using limited tactile supervision. It first trains a self‑supervised model that predicts masked visual features at hand‑surface locations, capturing latent contact dynamics. The model is then jointly fine‑tuned on a small labeled subset, improving contact discrimination and tactile magnitude estimation on human‑hand benchmarks and transferring to robotic hands without robot tactile data.