Name
Abstract | ||
|---|---|---|
Product packing is a typical application in warehouse 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9635914 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zifei Yang | 1 | 1 | 1.02 |
| Shuo Yang | 2 | 2 | 1.37 |
| Shuai Song | 3 | 0 | 0.34 |
| Wei Zhang | 4 | 75 | 59.96 |
| Ran Song | 5 | 0 | 4.73 |
| Jiyu Cheng | 6 | 0 | 2.03 |
| Yibin Li | 7 | 226 | 59.56 |