DDGC: Generative Deep Dexterous Grasping in Clutter
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arXiv
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| Format: | Preprint |
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2021
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| author | Lundell, Jens Verdoja, Francesco Kyrki, Ville |
| author_facet | Lundell, Jens Verdoja, Francesco Kyrki, Ville |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_04783 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | DDGC: Generative Deep Dexterous Grasping in Clutter Lundell, Jens Verdoja, Francesco Kyrki, Ville Robotics Machine Learning 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. |
| title | DDGC: Generative Deep Dexterous Grasping in Clutter |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2103.04783 |