Zhi Zhai — accepted AI-conference papers

2026

GeoDexGrasp: Geometry-aware Generation for Data-efficient and Physics-plausible Dexterous Grasping

Bing Han, Weiyuan Liu, Changlong Zhang, Chenxi Wang, Zhibin Zhao, Zhi Zhai

CVPR 2026

Achieving dexterous grasping remains a key challenge in robotics. Recent generative approaches enable diverse grasps through large-scale data-driven training, yet they often neglect geometric priors of objects, which leads to low data efficiency and poor physical plausibility. We propose GeoDexGrasp

Cited by 0 Source ↗Scholar ↗

2025

Learning Upright and Forward-Facing Object Poses using Category-level Canonical Representations

Bing Han, Ruitao Pan, Xinyu Zhang, Chenxi Wang, Zhi Zhai, Zhibin Zhao +1

IROS 2025

Constructing a unified canonical pose representation for 3D object categories is crucial for pose estimation and robotic scene understanding. Previous unified pose representations often relied on manual alignment, such as in ShapeNet and ModelNet. Recently, self-supervised canonicalization methods h

Cited by 0 Source ↗code ↗Scholar ↗