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

Abstract--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. Figure 1: Examples scenes of batch picking for four shapes: cube, cylinder, cuboid, hexagon. I. INTRODUCTION In warehouses, workers usually perform batch picking to investigations on the mechanism of holding multiple objects improve efficiency, also called multi-order picking. Nevertheless, For instance, a worker could be instructed to pick four boxes none of these works studied how to pick multiple of toothpaste or three jars of a cosmetic product from a bin.