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

TL;DR

This paper introduces a deep learning-based method to classify subtasks in human-robot interaction during collaborative drilling, enabling adaptive control that improves transparency and stability.

Contribution

It presents a novel ANN-based subtask classification approach that dynamically adjusts admittance control parameters in real time for better human-robot collaboration.

Findings

Achieved 98% accuracy in subtask classification across participants.

Reduced human effort by 20% during driving phase.

Lowered oscillation amplitude by 25% during contact phase.

Abstract

In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classification, the parameters of an admittance controller that regulates the interaction between human and robot are adjusted adaptively in real time to make the robot more transparent to the operator (i.e. less resistant) during the Driving phase and more stable during the Contact phase. The Idle phase is primarily used to detect the initiation of task. Experimental results have shown that the ANN model can learn to detect the subtasks under different admittance controller conditions with an accuracy of 98% for 12 participants. Finally, we show that the admittance 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 the Contact phase compared to an admittance controller with fixed parameters.