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

TL;DR

This paper introduces OPOS, a system that efficiently picks a specified number of objects in a single grasp using graph algorithms and neural networks, outperforming traditional single-object methods.

Contribution

The paper presents a novel multi-object picking system combining graph-based clustering and CNN-based prediction for efficient, exact multi-object grasping.

Findings

High success rates for picking two and three objects in one grasp.

OPOS outperforms single-object picking by 2-3 times in efficiency.

System generalizes well to unseen object sizes and shapes.

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.