Robotic Grasping of Fully-Occluded Objects using RF Perception

• URL: http://arxiv.org/abs/2012.15436v1
• Date: Thu, 31 Dec 2020 04:01:45 GMT
• Title: Robotic Grasping of Fully-Occluded Objects using RF Perception
• Authors: Tara Boroushaki, Junshan Leng, Ian Clester, Alberto Rodriguez, Fadel Adib
• Abstract summary: RF-Grasp is a robotic system that can grasp fully-occluded objects in unstructured environments. RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest.
• Score: 18.339320861642722
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present the design, implementation, and evaluation of RF-Grasp, a robotic system that can grasp fully-occluded objects in unknown and unstructured environments. Unlike prior systems that are constrained by the line-of-sight perception of vision and infrared sensors, RF-Grasp employs RF (Radio Frequency) perception to identify and locate target objects through occlusions, and perform efficient exploration and complex manipulation tasks in non-line-of-sight settings. RF-Grasp relies on an eye-in-hand camera and batteryless RFID tags attached to objects of interest. It introduces two main innovations: (1) an RF-visual servoing controller that uses the RFID's location to selectively explore the environment and plan an efficient trajectory toward an occluded target, and (2) an RF-visual deep reinforcement learning network that can learn and execute efficient, complex policies for decluttering and grasping. We implemented and evaluated an end-to-end physical prototype of RF-Grasp and a state-of-the-art baseline. We demonstrate it improves success rate and efficiency by up to 40-50% in cluttered settings. We also demonstrate RF-Grasp in novel tasks such mechanical search of fully-occluded objects behind obstacles, opening up new possibilities for robotic manipulation.

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