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.
Original language | English (US) |
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Pages (from-to) | 1303-1322 |
Number of pages | 20 |
Journal | Autonomous Robots |
Volume | 44 |
Issue number | 7 |
DOIs | |
State | Published - Sep 1 2020 |
Keywords
- EEG control
- EMG control
- Error-related potentials
- Gesture detection
- Human–robot interaction
- Hybrid control
- Plug-and-play supervisory control
ASJC Scopus subject areas
- Artificial Intelligence