Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.
@article{arxiv.2103.04783,
title = {DDGC: Generative Deep Dexterous Grasping in Clutter},
author = {Jens Lundell and Francesco Verdoja and Ville Kyrki},
journal= {arXiv preprint arXiv:2103.04783},
year = {2025}
}
Comments
Accepted to IEEE Robotics and Automation Letters 2021 and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)