Human intention inference through interacting multiple model filtering

We present an algorithm to learn human arm motion from demonstrations and infer the goal location (intention) of human reaching actions. To capture the complexity of human arm reaching motion, an artificial neural network (ANN) is used to represent the arm motion dynamics. The trajectories of the arm motion for reaching operation are modeled as stable dynamic systems with contracting behavior towards the goal location. The ANN is trained subjected to contraction analysis constraints. To adapt the motion model learned from a few demonstrations to novel scenarios or multiple objects, we use an interacting multiple model framework. The multiple models are obtained by translating the origin of the contracting system to different known goal locations. The posterior probabilities of the models are calculated through interactive model matched filtering carried out using extended Kalman filters (EKFs). The correct model is chosen according to the posterior probabilities to infer the correct intention. Demonstrations and measurements are recorded using a Microsoft Kinect sensor and experimental results are presented to validate the proposed algorithm.