Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brai...
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| Published in | Autonomous robots Vol. 44; no. 7; pp. 1303 - 1322 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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New York
Springer US
01.09.2020
Springer Nature B.V |
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| Abstract | Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a “plug-and-play” fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers. |
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| AbstractList | Effective human supervision of robots can be key for ensuring correct robot operation in a variety of potentially safety-critical scenarios. This paper takes a step towards fast and reliable human intervention in supervisory control tasks by combining two streams of human biosignals: muscle and brain activity acquired via EMG and EEG, respectively. It presents continuous classification of left and right hand-gestures using muscle signals, time-locked classification of error-related potentials using brain signals (unconsciously produced when observing an error), and a framework that combines these pipelines to detect and correct robot mistakes during multiple-choice tasks. The resulting hybrid system is evaluated in a “plug-and-play” fashion with 7 untrained subjects supervising an autonomous robot performing a target selection task. Offline analysis further explores the EMG classification performance, and investigates methods to select subsets of training data that may facilitate generalizable plug-and-play classifiers. |
| Author | Gil, Stephanie DelPreto, Joseph Hasani, Ramin Salazar-Gomez, Andres F. Rus, Daniela Guenther, Frank H. |
| Author_xml | – sequence: 1 givenname: Joseph orcidid: 0000-0001-8162-5317 surname: DelPreto fullname: DelPreto, Joseph email: delpreto@csail.mit.edu organization: Massachusetts Institute of Technology, Distributed Robotics Lab – sequence: 2 givenname: Andres F. surname: Salazar-Gomez fullname: Salazar-Gomez, Andres F. organization: Massachusetts Institute of Technology, Distributed Robotics Lab, Massachusetts Institute of Technology, Open Learning – sequence: 3 givenname: Stephanie surname: Gil fullname: Gil, Stephanie organization: Massachusetts Institute of Technology, Distributed Robotics Lab, Harvard University, REACT Lab – sequence: 4 givenname: Ramin surname: Hasani fullname: Hasani, Ramin organization: Massachusetts Institute of Technology, Distributed Robotics Lab, Technische Universität Wien, Cyber-Physical Systems Group – sequence: 5 givenname: Frank H. surname: Guenther fullname: Guenther, Frank H. organization: Boston University, Guenther – sequence: 6 givenname: Daniela surname: Rus fullname: Rus, Daniela organization: Massachusetts Institute of Technology, Distributed Robotics Lab |
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| Keywords | Error-related potentials EEG control Hybrid control Gesture detection Human–robot interaction EMG control Plug-and-play supervisory control |
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| SubjectTerms | Artificial Intelligence Brain Computer Imaging Control Control tasks Engineering Error correction Error detection Hybrid systems Mechatronics Multiple choice Muscles Pattern Recognition and Graphics Plug & play Robotics Robotics and Automation Robots Safety critical Signal classification Special Issue: Robotics: Science and Systems 2018 Supervisory control Vision |
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| Title | Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection |
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