Real-Time Monitoring of Human Task Advancement

Abstract

In collaborative robotics applications, the human behaviour is a major source of uncertainty. Predicting the evolution of the current human activity might be beneficial to the effectiveness of task planning, as it enables a higher level of coordination of robot and human activities. This paper addresses the problem of monitoring the advancement of human tasks in real-time giving an estimate of their expected duration. The proposed method relies on dynamic time warping to align the current activity with a reference template. No training phase is required, as the prototypical execution is learnt online from previous instances of the same activity. The applicability and performance of the method within an industrial context have been verified on a realistic assembly task.