  Recently, robotic grasp detection (GD) and object detection (OD) with
reasoning have been investigated using deep neural networks (DNNs). There have
been works to combine these multi-tasks using separate networks so that robots
can deal with situations of grasping specific target objects in the cluttered,
stacked, complex piles of novel objects from a single RGB-D camera. We propose
a single multi-task DNN that yields the information on GD, OD and relationship
reasoning among objects with a simple post-processing. Our proposed methods
yielded state-of-the-art performance with the accuracy of 98.6% and 74.2% and
the computation speed of 33 and 62 frame per second on VMRD and Cornell
datasets, respectively. Our methods also yielded 95.3% grasp success rate for
single novel object grasping with a 4-axis robot arm and 86.7% grasp success
rate in cluttered novel objects with a Baxter robot.
