Human intention inference and motion modeling using approximate E-M with online learning

In this paper, we present an algorithm to infer the intent of a human operator's arm movements based on the observations from a Microsoft Kinect sensor. Intentions are modeled as goal locations in 3-dimensional (3D) space where the human is intending to reach. Human intention inference is a critical step towards realizing safe human-robot collaboration. This work models the human arm's nonlinear motion dynamics using an unknown nonlinear function with intentions modeled as parameters. The unknown model is learned using a neural network (NN). Based on the learned model, an approximate expectation-maximization (E-M) algorithm is developed to infer human intentions. Furthermore, an identifier-based online model learning algorithm is developed to adapt to variations in the arm motion dynamics, trajectory of motion, goal locations, and initial conditions of different human subjects. We show the results of our algorithm using two sets of experiments conducted on data obtained from different users.