Daniel Beßler is a postdoctoral researcher at the Institute for Artificial Intelligence, University of Bremen, Germany. He received his doctoral degree in 2022. His main research interest are symbolic AI methods in the context of autonomous robots, and combinations of different methods in hybrid systems within this domain. In the scope of his doctoral thesis, he was focusing on knowledge representation and reasoning based on formal ontologies represented in the Web Ontology Langauge (OWL). He further is a co-author of the IEEE standard P1872.2 that proposes a standard ontology for the autonomous robotics domain, and is involved in activities that are directed towards the establishment of a community in the intersection of autonomous robotics and ontology research. One of these activities is the annual RobOntics workshop that attempts to foster and consolidate research conducted in both domains. In addition, Daniel has spent a lot of effort in developing software frameworks for the robotics community. Since 2015, he is the lead developer of the KnowRob knowledge processing system which is the most widely used KR&R system for service robots nowadays. Furthermore, he is the lead developer of the openEASE system, and one of the lead-authors of the SOMA ontology.

Abstract– It has been demonstrated many times that modern robotic platforms can generate competent bodily behavior comparable to the level of humans. However, the implementation of such behavior requires a lot of programming effort, and is often not feasible for the general case, i.e., regardless of the situational context in which the activity is performed. Furthermore, research and industry have an enormous need for intuitive robot programming. This is due to the high complexity of realizing an integrated robot control system, and adapting it to other robots, tasks and environments. The challenge is how a robot control program can be realized that can generate competent behavior depending on characteristics of the robot, the task it executes, and the environment where it operates. One way to approach this problem is to specialize the control program through the context-specific application of abstract knowledge.

In this work, it will be investigated how abstract knowledge, required for flexible and competent robot task execution, can be represented using a formal ontology. To this end, a domain ontology of robot activity context will be proposed. Using this ontology, robots can infer how tasks can be accomplished through movements and interactions with the environment, and how they can improvise to a certain extent to take advantage of action possibilities that objects provide in their environment. Accordingly, it will be shown that parts of the context-specific information required for flexible task execution can be derived from broadly applicable knowledge represented in an ontology. Furthermore, it will be shown that the domain vocabulary yields additional benefits for the representation of knowledge gained through experimentation and simulation. Such knowledge can be leveraged for learning, or be used to inspect the robot's behavior. The latter of which will be demonstrated in this work by means of a case study.

• openEASE
• KnowRob