My two main areas of work are knowledge acquisition, representation and reasoning techniques for autonomous robots, and knowledge-based methods for interpreting observations of human everyday activities.
In my work on knowledge representation for robots, I investigate how robots can be equipped with formally represented knowledge about actions, objects, environments, events, processes, etc. Such knowledge is needed to competently perform complex manipulation tasks. A special focus is on modern knowledge processing techniques that make use of information from the Web that has originally been created for humans, and that also use the Internet for exchanging knowledge between robots.
In this context, I am involved in the following projects:
In this line of research, I investigate how models everyday tasks like table setting and meal preparation can be constructed by combining observational data (mainly motion capture data) with formally represented knowledge about these activities. The objectives are, on the one hand, to gain a deeper understanding of how humans perform these tasks in order to transfer this knowledge to robots, and on the other hand, to evaluate and assess the task execution, e.g. to model and recognize effects caused by illnesses and disabilities.
In this context, I am involved in the following projects: