OPA-Pack: Object-Property-Aware Robotic Bin Packing

TL;DR

OPA-Pack is a novel robotic packing framework that considers object properties like fragility and chemistry, using advanced recognition and deep learning to improve packing safety and efficiency in real-world applications.

Contribution

It introduces a new object property recognition scheme, a property-aware packing network, and demonstrates practical improvements in safety and packing efficiency.

Findings

Increases accuracy of incompatible object separation from 52% to 95%.

Reduces pressure on fragile objects by 29.4%.

Maintains good packing compactness.

Abstract

Robotic bin packing aids in a wide range of real-world scenarios such as e-commerce and warehouses. Yet, existing works focus mainly on considering the shape of objects to optimize packing compactness and neglect object properties such as fragility, edibility, and chemistry that humans typically consider when packing objects. This paper presents OPA-Pack (Object-Property-Aware Packing framework), the first framework that equips the robot with object property considerations in planning the object packing. Technical-wise, we develop a novel object property recognition scheme with retrieval-augmented generation and chain-of-thought reasoning, and build a dataset with object property annotations for 1,032 everyday objects. Also, we formulate OPA-Net, aiming to jointly separate incompatible object pairs and reduce pressure on fragile objects, while compacting the packing. Further, OPA-Net consists of a property embedding layer to encode the property of candidate objects to be packed, together with a fragility heightmap and an avoidance heightmap to keep track of the packed objects. Then, we design a reward function and adopt a deep Q-learning scheme to train OPA-Net. Experimental results manifest that OPA-Pack greatly improves the accuracy of separating incompatible object pairs (from 52% to 95%) and largely reduces pressure on fragile objects (by 29.4%), while maintaining good packing compactness. Besides, we demonstrate the effectiveness of OPA-Pack on a real packing platform, showcasing its practicality in real-world scenarios.