Bimanual Shelf Picking Planner Based on Collapse Prediction

TL;DR

This paper introduces a bimanual manipulation planner that predicts shelf collapse to safely extract objects in cluttered warehouse shelves, achieving over 80% success in real-world tests.

Contribution

It presents a novel collapse prediction-based planner enabling safe bimanual extraction of objects from cluttered shelves, inspired by human strategies.

Findings

Achieved over 80% success rate in real-world experiments.

The planner effectively predicts collapse to ensure safe object extraction.

Utilized physics simulation data for training the collapse prediction model.

Abstract

In logistics warehouse, since many objects are randomly stacked on shelves, it becomes difficult for a robot to safely extract one of the objects without other objects falling from the shelf. In previous works, a robot needed to extract the target object after rearranging the neighboring objects. In contrast, humans extract an object from a shelf while supporting other neighboring objects. In this paper, we propose a bimanual manipulation planner based on collapse prediction trained with data generated from a physics simulator, which can safely extract a single object while supporting the other object. We confirmed that the proposed method achieves more than 80% success rate for safe extraction by real-world experiments using a dual-arm manipulator.