Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-Time, Highly Accurate Robotic Grasp Detection using Fully
  Convolutional Neural Networks with High-Resolution Images   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.

9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con