[ICRA23] GenDexGrasp: Generalizable Dexterous Grasping

Abstract

Generating dexterous grasping has been a long-standing and challenging robotic task. Despite recent progress, existing methods primarily suffer from two issues. First, most prior arts focus on a specific type of robot hand, lacking the generalizable capability of handling unseen ones. Second, prior arts oftentimes fail to rapidly generate diverse grasps with a high success rate. To jointly tackle these challenges with a unified solution, we propose GenDexGrasp, a novel hand-agnostic grasping algorithm for generalizable grasping. GenDexGrasp is trained on our proposed large-scale multi-hand grasping dataset MultiDex synthesized with force closure optimization. By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands. Compared with previous methods, GenDexGrasp achieves a three-way trade-off among success rate, inference speed, and diversity.

Type
Publication
In Proceedings of the IEEE International Conference on Robotics and Automation
Tengyu Liu
Tengyu Liu
Research Scientist
Yuyang Li
Yuyang Li
Ph.D. '24

My research interests lie in the intersection of 3D computer vision, computer graphics, and robotics. My long-term goal is to create intelligence that perceives, understands, and interacts with the physical / virtual environments.

Yixin Zhu
Yixin Zhu
Assistant Professor

I build humanlike AI.

Yaodong Yang
Yaodong Yang
Assistant Professor
Siyuan Huang
Siyuan Huang
Research Scientist

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