Robot Awareness of Human Behaviors and Interacting with Humans by A TwoStep Identifying Approach combining LSTM and SSD Robot cooperating with humans is a hot research topic recently. And the first challenge is how to specifically identify humansxe2x80x99 intention. In this paper, we propose a twostep approach based on LSTM and SSD for identifying humansxe2x80x99 specific intention. A LSTM with attention mechanism is firstly presented for recognizing humansxe2x80x99 actions based 3D skeleton data; then, the handheld objects are detected by a SSD neural network. Finally, a humanrobot interaction framework are constructed based on the proposed intention identification model and DMP model. The experiments show that the proposed method can effectively identifying humansxe2x80x99 intention. And the robot accomplishes to interact with the human with the built motion database and DMP.

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.