An Integrated Approach to Robotic Object Grasping and Manipulation
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Abstract (arXiv)
In response to the growing challenges of manual labor and efficiency in warehouse operations, Amazon has embarked on a significant transformation by incorporating robotics to assist with various tasks. While a substantial number of robots have been successfully deployed for tasks such as item transportation within warehouses, the complex process of object picking from shelves remains a significant challenge. This project addresses the issue by developing an innovative robotic system capable of autonomously fulfilling a simulated order by efficiently selecting specific items from shelves. A distinguishing feature of the proposed robotic system is its capacity to navigate the challenge of uncertain object positions within each bin of the shelf. The system is engineered to autonomously adapt its approach, employing strategies that enable it to efficiently locate and retrieve the desired items, even in the absence of pre-established knowledge about their placements.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 6 references that are in the map. First posted on arXiv 20 Nov 2024.
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References · 4
- 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
- 2022Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Imagescited by 120
- 2016Analysis and Observations from the First Amazon Picking Challengecited by 41
- 2020Single Shot 6D Object Pose Estimationcited by 19
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