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Picking up multiple objects at once is a grasping skill that makes a humanworker efficient in many domains. This paper presents a system to pick arequested number of objects by only picking once (OPO). The proposedOnly-Pick-Once System (OPOS) contains several graph-based algorithms thatconvert the layout of objects into a graph, cluster nodes in the graph, rankand select candidate clusters based on their topology. OPOS also has amulti-object picking predictor based on a convolutional neural network forestimating how many objects would be picked up with a given gripper locationand orientation. This paper presents four evaluation metrics and threeprotocols to evaluate the proposed OPOS. The results show OPOS has very highsuccess rates for two and three objects when only picking once. Using OPOS cansignificantly outperform two to three times single object picking in terms ofefficiency. The results also show OPOS can generalize to unseen size and shapeobjects.

Only Pick Once -- Multi-Object Picking Algorithms for Picking Exact Number of Objects Efficiently