Robotic Grasping of Fully-Occluded Objects using RF Perception
Name
RFGrasp-paper.pdf
Description
Accepted version
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11.26 MB
Format
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Checksum (MD5)
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Author(s)
Boroushaki, Tara
Leng, Junshan
Clester, Ian
Rodriguez, Alberto
Adib, Fadel
Date Issued
2021
Journal
2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Boroushaki, Tara, Leng, Junshan, Clester, Ian, Rodriguez, Alberto and Adib, Fadel. 2021. "Robotic Grasping of Fully-Occluded Objects using RF Perception." 2021 IEEE International Conference on Robotics and Automation (ICRA).
Version
Author's final manuscript
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. We
demonstrate it improves success rate and efficiency by up to 40-50% over a
state-of-the-art baseline. 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
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1109/ICRA48506.2021.9560956
