AI RESEARCH
ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientation
arXiv CS.CV
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ArXi:2604.11138v1 Announce Type: cross In-hand object reorientation requires precise estimation of the object pose to handle complex task dynamics. While RGB sensing offers rich semantic cues for pose tracking, existing solutions rely on multi-camera setups or costly ray tracing. We present a sim-to-real framework for monocular RGB in-hand reorientation that integrates 3D Gaussian Splatting (3DGS) to bridge the visual sim-to-real gap.