DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
Paper recorded by Signals 4 on 2026-09-17 in cs.CV. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-17 on arXiv · recorded by Signals 4 on 2026-09-18
Category: cs.CV · 计算机视觉 · first seen 2026-09-18
Abstract
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment-specific sensors. We introduce DexTouch-WM, an action-conditioned world model that learns from scalable human touch to jointly predict future RGB observations and bilateral tactile dynamics. Our insight is that human an
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Cite this page: DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation: the #9 most recent of 237 cs.CV papers we have recorded (as of 2026-09-17). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/dextouch-wm-learning-action-conditioned-tactile-world-models-from-human-touch-fo.html
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