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This content will become publicly available on November 15, 2022

Title: RFusion: Robotic Grasping via RF-Visual Sensing and Learning
We present the design, implementation, and evaluation of RFusion, a robotic system that can search for and retrieve RFID-tagged items in line-of-sight, non-line-of-sight, and fully-occluded settings. RFusion consists of a robotic arm that has a camera and antenna strapped around its gripper. Our design introduces two key innovations: the first is a method that geometrically fuses RF and visual information to reduce uncertainty about the target object's location, even when the item is fully occluded. The second is a novel reinforcement-learning network that uses the fused RF-visual information to efficiently localize, maneuver toward, and grasp target items. We built an end-to-end prototype of RFusion and tested it in challenging real-world environments. Our evaluation demonstrates that RFusion localizes target items with centimeter-scale accuracy and achieves 96% success rate in retrieving fully occluded objects, even if they are under a pile. The system paves the way for novel robotic retrieval tasks in complex environments such as warehouses, manufacturing plants, and smart homes.
Authors:
; ; ; ;
Award ID(s):
1844280
Publication Date:
NSF-PAR ID:
10319380
Journal Name:
SenSys '21: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems
Sponsoring Org:
National Science Foundation
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