  Picking up multiple objects at once is a grasping skill that makes a human
worker efficient in many domains. This paper presents a system to pick a
requested number of objects by only picking once (OPO). The proposed
Only-Pick-Once System (OPOS) contains several graph-based algorithms that
convert the layout of objects into a graph, cluster nodes in the graph, rank
and select candidate clusters based on their topology. OPOS also has a
multi-object picking predictor based on a convolutional neural network for
estimating how many objects would be picked up with a given gripper location
and orientation. This paper presents four evaluation metrics and three
protocols to evaluate the proposed OPOS. The results show OPOS has very high
success rates for two and three objects when only picking once. Using OPOS can
significantly outperform two to three times single object picking in terms of
efficiency. The results also show OPOS can generalize to unseen size and shape
objects.
