Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands
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arXiv:2608.29601v2 Announce Type: replace-cross Abstract: We present $N_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale mul...
arXiv:2608.29601v1 Announce Type: cross Abstract: We present $\mathcal{N}_0$-Foundation, a paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale m...
arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleope...
arXiv:2609.24976v1 Announce Type: cross Abstract: Dexterous manipulation depends on contact dynamics that are often only partially observable from vision. Recent World-Action Models (WAMs) couple pre...
arXiv:2606. 11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability.
Tactus is a textile‑integrated glove that combines a thin, conformal 3D‑knit design with strategically placed capacitive tactile sensors and IMU‑fused pose tracking to capture natural human manipulation. It offers a calibrated, cross‑glove consistency pipeline and per‑session subject correction, achieving a minimum detectable force of 0.59 N and fingertip pose error of 11.0 mm even under heavy occlusion. The authors benchmark Tactus against two commercial gloves, presenting the first quantitative cross‑platform evaluation of tactile force regression and hand pose estimation.