Perception and Reasoning for Scene Understanding in Human-Robot Interaction Scenarios

Abstract

Abstract. In this paper, a combination of perception modules and rea-soning engines is used for scene understanding in typical Human-Robot Interaction(HRI) scenarios. The major contribution of this work lies in a 3D object detection, recognition and pose estimation module, which can be trained using CAD models and works for noisy data, partial views and in cluttered scenes. This perception module is combined with first-order logic reasoning to provide a semantic description of scenes, which is used for process planning. This abstraction of the scene is an important concept in the design of intelligent robotic systems which can adapt to unstructured and rapidly changing environments since it pro-vides a separation of the process planning problem from its execution and scenario-specific parameters. This work is aimed at HRI applications in industrial settings and has been evaluated in several experiments and demonstration scenarios for autonomous process plan execution, human-robot interaction and co-operation. 1