Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning
arXiv:2606. 11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability.
HydroShear is a hydroelastic tactile simulator that models stick‑slip transitions, path‑dependent force and shear buildup, and full SE(3) object‑sensor interactions. It uses Signed Distance Functions to track indenter displacements on a sensor membrane, producing physics‑based force fields from arbitrary watertight geometries while remaining engine‑agnostic. Experiments on GelSight Minis show HydroShear reproduces real tactile shear more faithfully than existing methods, enabling zero‑shot sim‑to‑real reinforcement learning with a 93% success rate across four dexterous tasks.
arXiv:2606. 11767v1 Announce Type: cross Abstract: Blind grasping with a dexterous hand is a crucial manipulation capability.
arXiv:2609.15726v1 Announce Type: cross Abstract: Tactile sensing provides contact information that can be difficult to infer from vision alone, but tactile hardware for dexterous hands has not conve...
arXiv:2607. 09218v1 Announce Type: cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
arXiv:2607. 09218v2 Announce Type: replace-cross Abstract: Whole-arm manipulation involves direct contact with the environment while the robot completes a task by distributing contact across multiple links as contacts form, slide, and break.
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: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:2606. 11743v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks.
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...
Accurate contact prediction is useful for robotic manipulation only if it supports effective decisions. We investigate this connection using a compact, randomly initialized visuotactile world model, t...
DexTouch-WM is an action‑conditioned world model that learns from scalable human touch to predict future RGB observations and bilateral tactile dynamics for dexterous robot manipulation. By using compatible piezoresistive arrays on both human and robot hands and retargeting human motion into the robot action space, the model can be supervised with human interaction data while keeping a fixed amount of real‑robot supervision. Experiments show that adding up to 100 hours of human interaction improves robot‑domain visual, geometric, and contact prediction, and the model can serve as a surrogate environment for policy evaluation and synthetic trajectory generation.
arXiv:2610.10283v1 Announce Type: cross Abstract: Humans can grasp everyday objects with almost perfect success rates using fingertip tactile feedback, yet much of the robotic grasping literature emp...
arXiv:2607. 04234v2 Announce Type: replace-cross Abstract: Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction, holding the object stably without slip or drop while avoiding excessive deformation.