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.
By Zhuo Chen, Yihua Cheng, Ale\v{s} Leonardis, Hyung Jin Chang
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...
By Han Jiang, Etienne Vouga, Qixing Huang, Georgios Pavlakos
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.
By Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin, Yan Wu
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...
By Huajian Zeng, Lingyun Chen, Jiaqi Yang, Yuantai Zhang, Fan Shi, Peidong Liu, Xingxing Zuo
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.
By Niklas Berndt, Xianghui Xie, Enes Duran, Yixiao Wang, Gerard Pons-Moll
arXiv:2606. 17054v1 Announce Type: cross Abstract: Humans can grasp objects effortlessly, whereas multi-fingered robots are far from this level of generality.
By Kevin Yuanbo Wu, Tianxing Zhou, Isaac Tu, Billy Yan, Irmak Guzey, David Fouhey, Dandan Shan, Lerrel Pinto
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.
By Yuan Zeng, Yujia Shi, Tiao Tan, Xingting Li, Yaqi Qin, Zongqing Lu, Wenming Yang, Jing-Hao Xue, Qingmin Liao
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...
By Jongbin Lim, Taeyun Ha, Seongho Cha, Kanghyeon Cho, Mingi Choi, Subin Jeon, Jisoo Kim, Byungjun Kim, Hanbyul Joo
PiHand is a physics‑based policy learning framework that synthesizes realistic hand‑object interactions for dexterous manipulation. It first trains expert policies to track motion capture data, 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.
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.
By Naiyu Fang, Zhongjin Luo, Yuxin Mo, Siyuan Huang, Jianbo Liu, Yufei Liu, Zheyuan Zhou, Chenkai Jin, Xiaogang Wang, Hongsheng Li
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...
By Semin Kim, Haechan Shin, Jongyoo Kim
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.
By Julien Merand, Boris Meden, Mathieu Grossard, Liming Chen