GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation
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. We introduce a fundamentally different approach, grounded in the observation that the gripper and the object share identical surface geometry at their mutual contact points. We propose GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation, a novel deep generative model that learns a compact latent representation of a specific gripper's contact surface distribution, enabling the efficient sampling of valid grasp configurations without relying on object-specific t...
| Authors | Julien Merand, Boris Meden, Mathieu Grossard et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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