Prediction of Human Arm Target for Robot Reaching Movements The raise of collaborative robotics has allowed to create new spaces where robots and humans work in proximity. Consequently, to predict human movements and hisher final intention becomes crucial to anticipate robot next move, preserving safety and increasing efficiency. In this paper we propose a humanarm prediction algorithm that allows to infer if the human operator is moving towards the robot to intentionally interact with it. The human hand position is tracked by an RGBD camera online. By combining the Minimum Jerk model with SemiAdaptable Neural Networks we obtain a reliable prediction of the human hand trajectory and final target in a short amount of time. The proposed algorithm was tested in a multimovements scenario with FANUC LR Mate 200iD7L industrial robot.

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.