Bayesian human intention inference through multiple model filtering with gaze-based priors

An algorithm called gaze-based multiple model intention estimator (G-MMIE) is presented for early prediction of the goal location (intention) of human reaching actions. To capture the complexity of human arm reaching motion, a neural network (NN) is used to represent the arm motion dynamics. The trajectories of the arm motion for reaching tasks are modeled by using a dynamical system with contracting behavior towards the goal location. The contraction behavior ensures that the model trajectories will converge to the goal location. The NN training is subjected to contraction analysis constraints. In order to use the motion model learned from a few demonstrations with new scenarios and multiple objects, an interacting multiple model (IMM) framework is used. The multiple models are obtained by translating the origin of the contracting system to different known object locations. Each model corresponds to the reaching motion that ends at a certain object location. Since humans tend to look in the direction of the object they are reaching for, the prior probabilities of the models are calculated based on the human eye gaze. The posterior probabilities of the models are calculated through interacting model matched filtering carried out using extended Kalman filters (EKFs). The model or the object location with the highest posterior probability is chosen to be the estimate of the goal location. Experimental results suggest that the G-MMIE algorithm is able to adapt to arbitrary sequences of reaching motions and the gaze-based prior outperforms the uniform prior in terms of intention inference accuracy and average time of inference.