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Kunze, Lars
Researcher
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Table of ContentsCurrent Research ProjectsTeaching Inf. IX(Current Term) Publications |
Please note: Due to my parental leave, I am only available by email from June 22 to November 22, 2010.
About Me
I am currently a doctoral candidate supervised by Prof. Michael Beetz, PhD. I joined the Intelligent Autonomous Systems group at the Technische Universitaet Muenchen in 2008. Previously I studied computer science and cognitive science at the University of Osnabrueck. I received the MSc degree in computer science in 2008. While enrolled in the master's program, I worked as a graduate assistant in the Knowledge-Based Systems group lead by Prof. Dr. Joachim Hertzberg. In 2006, I received the BSc degree in cognitive science. During my undergraduate studies, I spent a semester abroad at the School of Informatics, part of the University of Edinburgh.
Research Interests
- Artificial Intelligence
- Knowledge Representation
- Commonsense Reasoning
- Naive Physics for Manipulation of Everyday Objects
Current Research Projects
- Naive Physics Reasoning : In this project we investigate a simulation-based approach to naive physics prediction in the context of autonomous robot everyday manipulation. We identify the abstractions underlying typical first-order axiomatizations as the key obstacles for making valid naive physics predictions. We propose that naive physics reasoning should not be performed based on abstractions but rather based on detailed physical simulations. This idea is realized as a naive physics reasoning system for autonomous manipulation robots that translates naive physics problems into parametrized physical simulation tasks, that logs the data structures and states traversed in simulation, and translates the logged data back into symbolic time-interval-based first-order representations.
- KnowRob / Knowledge 4 CoTeSys : The project targets at building knowledge representation and processing systems for mobile robots by combining description logics knowledge bases with data mining, (self-) observation modules and imported knowledge from the World Wide Web.
- CRAM : The main scientific goal of the proposed project is to build the CRAM (Cognitive Robot Abstract Machine) as a software toolbox for the design, the implementation, and the deployment of cognition-enabled autonomous robots performing everyday manipulation activities. CRAM provides a language for programming such cognitive control systems. This language includes data structures, primitive statements and control structures that are specifically designed to enable and support mobile manipulation as well as cognition-enabled control. CRAM is needed because a robot performing everyday manipulation tasks must continually decide on its course of action and on how actions have to be performed. Even seemingly simple tasks such as picking up an object from a table require complex decision making. To pick up an object, the robot must decide where to stand in order to reach the object, which hand(s) to use, how to reach for it, which grasp type to apply, where to grasp, how much grasp force to apply, how to lift the object, how much force to apply to lift it, where to hold the object, and how to hold it. The decision problems are even more complex because many decisions depend on the task context, which requires the robot to take many factors into account to achieve the best performance or at least a performance that is good enough.
Teaching Inf. IX (Current Term)
- Künstliche Intelligenz (Proseminar)
Teaching General
Upcoming semester:
Winter semester 2010/11:
- ...
Past semesters:
Winter semester 2009/10:
Summer semester 2009:
Theses / Student Projects
Open Topics:
Running:
Completed:
Selected Publications
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Putting People's Common Sense into Knowledge Bases of Household Robots, 2010, 33rd Annual German Conference on Artificial Intelligence (KI 2010),(BibTeX)
