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In this paper, we propose a supervised learning approach based on anArtificial Neural Network (ANN) model for real-time classification of subtasksin a physical human-robot interaction (pHRI) task involving contact with astiff environment. In this regard, we consider three subtasks for a given pHRItask: Idle, Driving, and Contact. Based on this classification, the parametersof an admittance controller that regulates the interaction between human androbot are adjusted adaptively in real time to make the robot more transparentto the operator (i.e. less resistant) during the Driving phase and more stableduring the Contact phase. The Idle phase is primarily used to detect theinitiation of task. Experimental results have shown that the ANN model canlearn to detect the subtasks under different admittance controller conditionswith an accuracy of 98% for 12 participants. Finally, we show that theadmittance adaptation based on the proposed subtask classifier leads to 20%lower human effort (i.e. higher transparency) in the Driving phase and 25%lower oscillation amplitude (i.e. higher stability) during drilling in theContact phase compared to an admittance controller with fixed parameters.

An adaptive admittance controller for collaborative drilling with a robot based on subtask classification via deep learning