CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis
Overview
CoToGrasp is a generative framework that synthesizes stable grasps based on contact topologies without object-annotated data. It achieves state-of-the-art performance on DexGraspNet and demonstrates physical feasibility on a robot platform. (2026-10-09).
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“ity and kinematic feasibility of our synthesized contact topologies on a physical robot platform. Code is available on our project website at https://cea-list.github.io/cotograspweb/ .”
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- 2026-10-10T23:16:28.512Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsCoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning
This research proposes CoToGrasp, a generative framework that synthesizes diverse and stable grasps conditioned on specific contact topologies, without requiring object-annotated datasets. The method uses a canonical workspace to decouple semantic intent from object geometry, enabling zero-shot generalization. It achieves state-of-the-art performance on DexGraspNet and demonstrates physical feasibility on a robot platform.
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