  In this paper we present an approach for learning to imitate human behavior
on a semantic level by markerless visual observation. We analyze a set of
spatial constraints on human pose data extracted using convolutional pose
machines and object informations extracted from 2D image sequences. A scene
analysis, based on an ontology of objects and affordances, is combined with
continuous human pose estimation and spatial object relations. Using a set of
constraints we associate the observed human actions with a set of executable
robot commands. We demonstrate our approach in a kitchen task, where the robot
learns to prepare a meal.
