arXiv Computer Vision
Proximity3D: Shape from Capacitive Proximity on Sensing Manifold
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Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch
arXiv:2607. 13479v1 Announce Type: cross Abstract: Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the manipulating hand, or under heavy self-occlusion.
arXiv Machine Learning
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SoTa: Soft Tactile Skins for Dexterous Manipulation
SoTa is a low‑cost, capacitive tactile skin that offers full‑hand coverage for both humans and robots, featuring a shared layout of 202 taxels across finger and palm regions. The multilayer design with fabric electrodes allows in‑house fabrication for under $10 in materials, and the sensor maintains over 97% of its response after 10,000 loading‑unloading cycles and 1,280 tight‑fist folds. Using SoTa, human‑robot co‑training improves in‑distribution success on three contact‑rich manipulation tasks, and adding human demonstrations more than doubles mean success across eight evaluation conditions, from 22.8% to 45.9%, including all out‑of‑distribution scenarios.
arXiv Computer Vision
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HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
arXiv:2604.14944v3 Announce Type: replace-cross Abstract: We present HRDexDB, a real-world 4D dexterous grasping dataset capturing 3D hand-object interaction trajectories over time across five embodi...
arXiv Machine Learning
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More with LESS -- Local Scene Representations for Tactile Imaging
arXiv:2606. 14344v1 Announce Type: new Abstract: Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation.
arXiv Computer Vision
Aug 31
GraspHOI: Full-Body 3D Human-Object Reconstruction with Finger-Level Grasps from a Single In-the-Wild Image
arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic o...
arXiv Computer Vision
Aug 31
Object Reconstruction under Occlusion with Generative Priors and Contact-induced Constraints
The paper presents a method for reconstructing object geometry under occlusion by combining generative shape priors with contact-based constraints. Generative models provide plausible guesses for unseen parts, while contact information from videos or physical interactions supplies sparse boundary constraints. The authors integrate these cues through a contact-guided 3D generation framework, demonstrating improved reconstruction on synthetic and real-world datasets compared to baseline approaches.