Pick Planning Strategies for Large-Scale Package Manipulation

Autor:	Li, Shuai, Keipour, Azarakhsh, Jamieson, Kevin, Hudson, Nicolas, Zhao, Sicong, Swan, Charles, Bekris, Kostas
Rok vydání:	2023
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations. This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is used for picking and singulating up to 6 million packages per day and so far has manipulated over 2 billion packages. It describes the various heuristic methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system. Comment: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Learning Meets Model-based Methods for Manipulation and Grasping Workshop
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.13224 Zobrazit plný text záznamu View this record from Arxiv