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Vulcan Pick: A robotic system for picking targeted objects from fabric pods

By Kiru Park, Johannes Kulick, Alexander Melkozerov, Roc Arandes Vilagrasa, Teguh Santoso Lembono, Vanessa Neubauer, Artem Minichev, Kade Turner, Oana Agrigoroaiei, Pascal Klink, Jonathan Lee, Kshitij Dwivedi, Mazin Eltayeb, Ingmar Posner, Aaron Parness, Can Erdogan
2025
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This paper presents an integrated robotic system designed for autonomous picking of targeted objects from cluttered and deformable shelves—a critical task in Amazon warehouse operations for processing customer orders. The system addresses common challenges in robotic picking including diverse object handling, densely packed storage, and dynamic inventories. However, shelf-picking introduces additional complexities, particularly the risk of inadvertently pulling adjacent items out, requiring advances in 3D scene understanding and adaptive motion control with continuous visual feedback. We introduce an end-to-end solution that combines proven classical methods with state-of-the-art approaches in computer vision, motion planning, and customized hardware. Our system has been operating in a live warehouse environment for over six months, processing more than 12,000 customer orders. This paper outlines the approach taken, presents key performance metrics, and discusses failure cases encountered. In highlighting the insights gained during this long-term deployment, and in particular the challenges in developing scalable robotic applications for warehouse automation, our aim is to communicate the current state of the art and propose future directions of development for robotic picking solutions.
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US, NJ, Newark
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