Shelf Replenishment Based on Object Arrangement Detection and Collapse Prediction for Bimanual Manipulation

Abstract

Object manipulation automation in logistic warehouses has recently been actively researched. However, shelf replenishment is a challenge that requires the precise and careful handling of densely piled objects. The irregular arrangement of objects on a shelf makes this task particularly difficult. This paper presents an approach for generating a safe replenishment process from a single depth image, which is provided as an input to two networks to identify arrangement patterns and predict the occurrence of collapsing objects. The proposed inference-based strategy provides an appropriate decision and course of action on whether to create an insertion space while considering the safety of the shelf content. In particular, we exploit the bimanual dexterous manipulation capabilities of the associated robot to resolve the task safely, without re-organizing the entire shelf. Experiments with a real bimanual robot were performed in three typical scenarios: shelved, stacked, and random. The objects were randomly placed in each scenario. The experimental results verify the performance of our proposed method in randomized situations on a shelf with a real bimanual robot.

Keywords

• shelf replenishment
• bimanual manipulation
• deep learning in grasping and manipulation