An incremental learning approach for physical human-robot collaboration

Physical Human-Robot Collaboration requires humans and robots to perform joint tasks in a shared workspace. Since robot’s characteristic strengths are to cope well with high payloads, they are utilized to assist human operators during heavy pulling or pushing activities. A widely used sensor to detect human muscle fatigue and thus, to trigger an assistance request, is an Electromyography (EMG). Many previous approaches to process EMG data are based on training Machine Learning models offline or include a large degree of manual fine tuning. However, due to recent advances in Machine Learning such as incremental learning, there is an opportunity to apply online learning which reduces programming effort and also copes well with subject specific characteristics of EMG signals. Initial results show promising potential, yet, unveil a conflict between convergence time and classification accuracy.