arXiv Computer Vision
BiView-Touch: Learning Bimanual Tactile Representations by Cross-Hand Completion
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arXiv Computer Vision
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VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation
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arXiv Computer Vision
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DexTacWAM: A Visuo-Tactile World-Action Model for Dexterous Manipulation
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BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors
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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.
arXiv AI
Jul 29
InDex: Empowering VLA Models with Intent-Conditioned Arm-Hand Coordination for Dexterous Manipulation
arXiv:2606. 12109v2 Announce Type: replace-cross Abstract: Pre-trained Vision-Language-Action (VLA) models provide useful semantic and spatial priors, yet their parallel-gripper action interfaces do not specify how those priors should be realized by a dexterous hand.