Realtime monitoring of human task advancement In collaborative robotics applications, 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 realtime 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.

Human Intention Inference and OnLine Human Hand Motion Prediction for HumanRobot Collaboration With recent development of robotic technology, it is increasingly common that robot coexist with human, in which humans and robots share a common workspace and work in close proximity. To maintain efficiency and ensure safety under these circumstances, robot should have the ability to predict the future human motion based on the observed ongoing motion. In this paper, we present a methodology for online inference of human intention and prediction of human hand motion. The proposed framework is built using Probabilistic Dynamic Movement Primitive (PDMP). In the offline stage, a set of PDMPs is constructed based on the recorded demonstrations and they will then be used for inferring human intention and predicting human hand motion in the online stage. A proof of concept evaluation is carried out in a tabletop manipulation task. Experimental result shows the proposed framework achieve high performance in human intention inference and in the trajectory similarity between the predicted and the actual hand movement under the normally defined environment. We also show the proposed framework can adapt and generalize to the newly defined environment.