Human Pose and Action Recognition for Interactive Robots

There is currently a division between real-world human performance and the decision making of socially interactive robots. Specifically, the decision making of robots needs to have information about the decision making of its human collaborators. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards crossing this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies.