Shape completion enabled robotic grasping

@article{Varley2017ShapeCE,
  title={Shape completion enabled robotic grasping},
  author={Jacob Varley and Chad DeChant and Adam Richardson and Avinash Nair and Joaqu{\'i}n Ruales and Peter K. Allen},
  journal={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2017},
  pages={2442-2447}
}
This work provides an architecture to enable robotic grasp planning via shape completion. Shape completion is accomplished through the use of a 3D convolutional neural network (CNN). The network is trained on our own new open source dataset of over 440,000 3D exemplars captured from varying viewpoints. At runtime, a 2.5D pointcloud captured from a single point of view is fed into the CNN, which fills in the occluded regions of the scene, allowing grasps to be planned and executed on the… CONTINUE READING
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