Reproduce “DexTaG: Tactile-as-Guidance in Reinforcement Learning for Dexterous Manipulation” on OpenResearch
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About this paper
Han Yang, Yian Wang, Yunlong Song, Zhenjia Xu, Chuang Gan
DexTaG uses glove-measured contact patterns to guide reinforcement-learning retargeting for picking up and reorienting flat-lying tools when pose estimates misplace fingertips. It trains one retargeter across same-object demonstrations, then distills it into a controller that follows object trajectories without tactile hardware. On marker-pen simulation, it reaches 87.5% trajectory completion versus 48.6% for a geometry-gated variant, a setup-specific whole-method comparison.
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