PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning
Zifei Yang, Shuo Yang, Shuai Song, Wei Zhang, Ran Song, Jiyu Cheng, Yibin Li
Abstract
Product packing is a typical application in ware-house automation that aims to pick objects from unstructured piles and place them into bins with optimized placing policy. However, it still remains a significant challenge to finish the product packing tasks in general logistics scenarios where the objects are variable-sized and the configurations are complex. In this work, we present the PackerBot, a complete robotic pipeline for performing variable-sized product packing in unstructured scenes. First, by leveraging the imperfect experience of human packer, we propose a heuristic DRL framework for learning optimal online 3D bin packing policy. Then we integrate it with a 6-DoF suction-based picking module and a product size estimation module, leading to a complete product packing system, namely the PackerBot. Extensive experimental results show that our method achieves the state-of-the-art performance in both simulated and real-world tests. The video demonstration is available at: https://vsislab.github.io/packerbot.
BibTeX
@inproceedings{iros2021_packerbotvariabl,
title = {PackerBot: Variable-Sized Product Packing with Heuristic Deep Reinforcement Learning},
author = {Zifei Yang and Shuo Yang and Shuai Song and Wei Zhang and Ran Song and Jiyu Cheng and Yibin Li},
booktitle = {IROS 2021},
year = {2021}
}