Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life-a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them. Safe robot control systems are a current research focus in control theory. The control system designs, however, are a bit paranoid: programmers build "software fences" around people, effectively preventing physical interactions. To physically interact in a competent manner robotic agents have to reason about the task context, the human, and her intentions. In this paper, we propose to extend cognition-enabled robot control by introducing humans, physical interaction events, and safe movements as first class objects into the plan language. We show the power of the safety-aware control approach in a real-world scenario with a leading-edge autonomous manipulation platform. Finally, we share our experimental recordings through an online knowledge processing system, and invite the reader to explore the data with queries based on the concepts discussed in this paper.

No abstract

No abstract

No abstract