  Robotic grasp detection for novel objects is a challenging task, but for the
last few years, deep learning based approaches have achieved remarkable
performance improvements, up to 96.1% accuracy, with RGB-D data. In this paper,
we propose fully convolutional neural network (FCNN) based methods for robotic
grasp detection. Our methods also achieved state-of-the-art detection accuracy
(up to 96.6%) with state-of- the-art real-time computation time for
high-resolution images (6-20ms per 360x360 image) on Cornell dataset. Due to
FCNN, our proposed method can be applied to images with any size for detecting
multigrasps on multiobjects. Proposed methods were evaluated using 4-axis robot
arm with small parallel gripper and RGB-D camera for grasping challenging
small, novel objects. With accurate vision-robot coordinate calibration through
our proposed learning-based, fully automatic approach, our proposed method
yielded 90% success rate.
