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Document type:
Konferenzbeitrag 
Author(s):
Wang, P.; Manhardt, F.; Minciullo, L.; Garattoni, L.; Meier, S.; Navab, N.; Busam, B. 
Title:
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration 
Abstract:
The ability to successfully grasp objects is crucial in robotics, as it enables several interactive downstream applications. To this end, most approaches either compute the full 6D pose for the object of interest or learn to predict a set of grasping points. While the former approaches do not scale well to multiple object instances or classes yet, the latter require large annotated datasets and are hampered by their poor generalization capabilities to new geometries. To overcome these shortco...    »
 
Keywords:
IROS,IROS2021,CAMP,CAMPComputerVision,ComputerVision,ARXIV,Rigid3DObjectDetection,Deep Learning,deeplearning 
Year:
2021