Bridging the Gap Between Supervised Autonomy and Teleoperation

Human teleoperation of robots and autonomous operations go hand in hand in many of todays service robots. While robot teleoperation is typically performed on low to medium levels of abstraction, automated planning has to take place on a higher abstraction level, i.e. by means of semantic reasoning. Accordingly, an abstract state of the world has to be maintained in order to enable an operator to switch seamlessly between both operational modes. We propose a novel approach that combines simulation-based geometric tracking and semantic state inference by means of so called State Inference Entities to overcome this issue. The system is demonstrated in real-world experiments conducted with the humanoid robot Rollin’ Justin.