Abstract:
Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing
the vision of pervasive human-robot collaborations. For these
cases, the ability to use human cognition in the loop can be key
for recuperating safe robot operation. This paper combines two
streams of human biosignals, electrical muscle and brain activity
via EMG and EEG, respectively, to achieve fast and accurate
human intervention in a supervisory control task. In particular,
this paper presents an end-to-end system for continuous rollingwindow classification of gestures that allows the human to
actively correct the robot on demand, discrete classification of
Error-Related Potential signals (unconsciously produced by the
human supervisor’s brain when observing a robot error), and
a framework that integrates these two classification streams for
fast and effective human intervention. The system also allows
“plug-and-play” operation, demonstrating accurate performance
even with new users whose biosignals have not been used for
training the classifiers. The resulting hybrid control system for
safety-critical situations is evaluated with 7 untrained human
subjects in a supervisory control scenario where an autonomous
robot performs a multi-target selection task.