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:2606.29924v2 Announce Type: replace Abstract: Generating 3D hand-object interactions is essential for applications in robotics, AR/VR, and synthetic data generation, where flexible controllabil...
arXiv:2608. 19759v1 Announce Type: cross Abstract: Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects because they are trained on limited, object-specific datasets.
arXiv:2606. 09243v1 Announce Type: cross Abstract: Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware.
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.
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:2608. 19776v1 Announce Type: cross Abstract: Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks.
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:2606. 06872v1 Announce Type: cross Abstract: Estimating hand-surface contact pressure from an egocentric view is crucial for AR/VR devices, robotic imitation, and ergonomic analysis.
OmniDex presents a large-scale benchmark for dexterous hand grasping, featuring over 2.6 million cluttered scenes and 0.4 B scene-specific grasp ground truths generated via a scalable seed-and-filter strategy. The paper introduces the OmniDex model, which uses Soft Winner-Takes-All learning, human-inspired physical constraints, and physics-driven ranking to address multimodality and precision errors in generative grasp models. Experimental results demonstrate that OmniDex achieves state‑of‑the‑art performance and strong generalization across diverse scenes, views, and unseen objects.
arXiv:2608.13014v2 Announce Type: replace Abstract: Understanding hand-object interaction from egocentric vision is essential for modeling how people physically engage with the surrounding world. Yet...
Estimating physical pressure from vision is essential for understanding contact-rich hand-object interaction. However, prior vision-based pressure estimation methods are largely limited to planar surfaces and single image input, making them difficult to apply to dynamic hand-object interaction with diverse objects.
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.