Robotic Grasping of Fully-Occluded Objects using RF Perception
Name
Boroushaki-tarab-SM-MAS-2021-thesis.pdf
Description
Thesis PDF
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11.33 MB
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Adobe PDF
Checksum (MD5)
44ba24f9d9c5573669bbd4c3c6bc3164
Author(s)
Boroushaki, Tara
Advisor(s)
Adib, Fadel
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
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. Qualitative results (videos) available at rfgrasp.media.mit.edu
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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