Projekteja vuodessa
Abstrakti
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 two state-of-the-art methods on 1200 simulated cluttered scenes and 7 real-world scenes. The results show that DDGC outperforms the baselines in synthesizing high-quality grasps and removing clutter. DDGC is also 4-5 times faster than GraspIt!. This, in turn, opens 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. Code and videos are available at https://irobotics.aalto.fi/ddgc/.
Alkuperäiskieli | Englanti |
---|---|
Artikkeli | 9483683 |
Sivut | 6899 - 6906 |
Sivumäärä | 8 |
Julkaisu | IEEE Robotics and Automation Letters |
Vuosikerta | 6 |
Numero | 4 |
DOI - pysyväislinkit | |
Tila | Julkaistu - lokak. 2021 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Sormenjälki
Sukella tutkimusaiheisiin 'DDGC: Generative Deep Dexterous Grasping in Clutter'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Projektit
- 1 Päättynyt
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ROSE: Robotit ja hyvinvointipalvelujen tulevaisuus
Kyrki, V., Brander, T., Racca, M., Lundell, J. & Verdoja, F.
01/01/2018 → 30/04/2021
Projekti: Academy of Finland: Strategic research funding