System for Autonomous Management of Retail Shelves Using an Omnidirectional Dual-arm Robot with a Novel Soft Gripper

Nijil George, Somdeb Saha, Shubham Parab, Vismay Vakharia, Rolif Lima, Vighnesh Vatsal, Kaushik Das

Abstract: Managing shelves in retail stores includes re-stocking, rearrangement and replenishment of products. As these are some of the most labor-intensive activities, there has been widespread demand from retailers for automation in this domain. However, major challenges still remain in perception, navigation and manipulation while implementing an autonomous robotic system for this purpose. We present a system aimed at addressing some of these challenges through novel approaches. In terms of perception, we have developed a transformer-based local anomaly detection algorithm that can identify misplaced items without the need for a central database. Navigation of the omnidirectional mobile base is performed through stereo vision and LiDAR sensors. Finally, identifying grasping and manipulation as one of the key shortcomings of present robotic systems in this domain, we have developed a customized soft robotic gripper targeted at retail objects. It has compliant cable-driven fingers, and a palm configuration that can be adapted in real-time based on the target object's geometry. Coupled with a conventional two-fingered gripper in a dual-arm setup, this system is equipped to handle most objects encountered in a retail setting. We describe the underlying hardware and algorithms for each component of the system, evaluating their individual performance. We then evaluate the whole system in a mock retail setup, demonstrating promising results for autonomous management of shelves.