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.

中文翻译：

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使用肌肉和大脑信号进行即插即用的监督控制，用于实时手势和错误检测

对机器人进行有效的人工监督对于确保在各种潜在的对安全至关重要的情况下机器人的正确操作至关重要。本文通过结合两种人类生物信号流，分别通过EMG和EEG获得的肌肉和大脑活动，朝着快速，可靠的人为干预控制任务迈出了一步。它提供了使用肌肉信号对左手和右手手势的连续分类，使用脑信号对错误相关电位的时间锁定分类（观察错误时会无意识地产生）以及将这些管线组合在一起以检测和纠正机器人错误的框架。选择题任务。最终的混合系统以“即插即用”的方式进行评估，其中有7名未经训练的受试者在监督自主机器人执行目标选择任务的过程中。