Title: Real-world Multi-object, Multi-grasp Detection,
Abstract: A deep learning architecture is proposed to predict graspable locations for
robotic manipulation. It considers situations where no, one, or multiple
object(s) are seen. By defining the learning problem to be classification with
null hypothesis competition instead of regression, the deep neural network with
RGB-D image input predicts multiple grasp candidates for a single object or
multiple objects, in a single shot. The method outperforms state-of-the-art
approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise
and object- wise splits, respectively. Evaluation on a multi-object dataset
illustrates the generalization capability of the architecture. Grasping
experiments achieve 96.0% grasp localization and 88.0% grasping success rates
on a test set of household objects. The real-time process takes less than .25 s
from image to plan.