arXiv Computer Vision
DCGrasp: Distance-aware Controllable Grasp Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 8
VolCo: Volumetric Contact for High-Fidelity Human Grasp Generation
The paper introduces VolCo, a volumetric contact representation that extends surface points into 3D grids to model hand-object interactions more accurately. Using a 3D variational autoencoder for local contact details and a prior-guided diffusion model for global hand geometry, the VolCoDiff framework achieves tighter grasps with reduced penetration on benchmark datasets. The authors provide code for reproducibility.
arXiv Computer Vision
Oct 8
StableGrasp: Reconstructing Physically Stable Human Hand Grasps from Single Images
arXiv:2610.09195v1 Announce Type: new Abstract: Reconstructing a physically stable human grasp from a single RGB image is challenging because physically modeling grasps is itself difficult, and the p...
arXiv AI
Oct 19
MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping
MANGO‑Grasp introduces an anisotropic interaction framework for cross‑embodiment dexterous grasping. It represents objects as geometry‑oriented 3D Gaussian primitives to preserve local surface geometry, encodes hands with pretrained descriptors that capture morphology and kinematics, and models hand‑object interaction using anisotropic Mahalanobis fields. The method achieves state‑of‑the‑art performance on CMAP and MultiGripperGrasp benchmarks, outperforms baselines by up to 8.24 percentage points in simulation, and demonstrates strong zero‑shot transfer to the unseen SharpaWave hand with up to 16.57 percentage point improvement, achieving 86% success in real‑world experiments.
arXiv AI
2d ago
FlowHOI: Flow-based Semantics-Grounded Generation of Hand-Object Interactions for Dexterous Robot Manipulation
arXiv:2602.13444v2 Announce Type: replace-cross Abstract: Recent vision-language-action (VLA) models can generate plausible end-effector motions, yet they often fail in long-horizon, contact-rich tas...
arXiv Computer Vision
6d ago
PiHand: Latent Hand-Object Interaction Priors for Physics-Based Policy Learning
PiHand is a physics‑based framework that learns latent hand‑object interaction priors for dexterous manipulation. It first trains expert policies to track motion capture demonstrations, then distills these into a goal‑conditioned VAE that captures coupled hand‑object motion. Using bounded residual exploration in this latent space, PiHand produces general tabletop manipulation policies that generalize to unseen objects and trajectories, outperforming baselines across multiple tasks.
arXiv AI
Jun 19
Human Universal Grasping
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.