Plug-and-play supervisory control using muscle and brain signals for real-time gesture and error detection
| Autor: | Joseph DelPreto, Frank H. Guenther, Andres F. Salazar-Gomez, Daniela Rus, Stephanie Gil, Ramin M. Hasani |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Speech recognition Robotics 02 engineering and technology Autonomous robot Human–robot interaction Task (computing) 020901 industrial engineering & automation Supervisory control Artificial Intelligence Gesture recognition 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence business Gesture |
| Zdroj: | Autonomous Robots. 44:1303-1322 |
| ISSN: | 1573-7527 0929-5593 |
| DOI: | 10.1007/s10514-020-09916-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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