Mechanical Search on Shelves using a Novel "Bluction" Tool

TL;DR

This paper introduces a novel tool and method for mechanical search on shelves, combining a new 'bluction' tool, improved simulation and perception, and an optimized search policy, significantly enhancing object retrieval success rates.

Contribution

The paper presents a new 'bluction' tool, an improved simulation pipeline, and a novel search policy for shelf object retrieval, advancing the state of mechanical search techniques.

Findings

Suction grasping improves success rate by 26% in simulation.

Suction grasping improves success rate by 67% in physical tests.

The proposed method outperforms push-only policies in shelf search tasks.

Abstract

Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateral-access mechanical search problem. To address this problem, we introduce: (1) a novel bluction tool, which combines a thin pushing blade and suction cup gripper, (2) an improved LAX-RAY simulation pipeline and perception model that combines ray-casting with 2D Minkowski sums to efficiently generate target occupancy distributions, and (3) a novel SLAX-RAY search policy, which optimally reduces target object distribution support area using the bluction tool. Experimental data from 2000 simulated shelf trials and 18 trials with a physical Fetch robot equipped with the bluction tool suggest that using suction grasping actions improves the success rate over the highest performing push-only policy by 26% in simulation and 67% in physical environments.