期刊
AUTONOMOUS ROBOTS
卷 31, 期 1, 页码 1-20出版社
SPRINGER
DOI: 10.1007/s10514-011-9228-1
关键词
Grasping; Robotics; Data-driven
资金
- NIH BRP [1RO1 NS 050256-01A2]
- Google research grant
This paper propose a novel framework for a data driven grasp planner that indexes partial sensor data into a database of 3D models with known grasps and transfers grasps from those models to novel objects. We show how to construct such a database and also demonstrate multiple methods for matching into it, aligning the matched models with the known sensor data of the object to be grasped, and selecting an appropriate grasp to use. Our approach is experimentally validated in both simulated trials and trials with robots.
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