Prof. Beetz, Michael, PhD Professor Head of the IAS Group Computer Science Department, Chair IX E-Mail: beetz(at)in.tum.de Telephone: +49 (89) 289-17759 Office: 02.09.044

Table of Contents Research Topics Current Research Projects

 

Dagstuhl seminar 2009 - Cognition, Control and Learning for Robot Manipulation in Human Environments

The seminar will be held from 16.08.2009 to 21.08.2009 at castle Dagstuhl Germany. The goal of the seminar is to make significant progress
towards closing the gap between human and robot manipulation in uncertain environments inhabited by humans. Therefore, this Dagstuhl seminar
attempts to get internationally renowned researchers across a range of disciplines together in order to discuss robot manipulation from different points
of view. The event is sponsored by the Leibniz-Gemeinschaft.

INFO: http://www9.in.tum.de/events/Dagstuhl-2009

Past Events:

30th German Conference on Artificial Intelligence (KI-2007) - Osnabrück, Germany, September 10-13, 2007
The Fifth International Cognitive Robotics Workshop (The AAAI-06 Workshop on Cognitive Robotics) - Boston, Massachusetts, USA, July 16-17, 2006

 

Research Interests:

• Integrated Intelligent Systems 
• Plan-based Control of Autonomous Agents
• Integration of Programming and Learning for Autonomous Agent Control
• Automatic Sensor-based Analysis of Intentional Activity
• Probabilistic State Estimation

Current Research Projects

• ASPOGAMO : The research project 'Sensor-based, Automatic Analysis of Football Games' is an ambitious, mid-term research project that studies the automation of these tasks. The main objectives of the project are (1) the investigation of novel computational mechanisms that enable computer systems to recognize intentional activities, (2) the development of an integrated software system to automate game interpretation and analysis, and (3) the demonstration of the impact of automated game analysis on application areas, such as sport science, football coaching, and sports entertainment.

• AwareKitchen : Intelligent sensor equipped environments can be of much greater help if they are capable of recognizing the actions and activities of their users, and inferring their intentions. Understanding human activities and characterizing them into expressive and detailed activity models is one of the key issues of today's current pervasive computing systems. If such a system could recognize and understand automatically its user's behavior, it could interact in a more efficient and friendly manner. Unfortunately, the current model construction techniques are based on supervised learning and require specifications from their human counterparts, such as labeling the acquired sensor data. Our vision is to build technical cognitive systems that create and use models in a straightforward manner, by combining already existing online information with the system's context history.

• Cogito : A key challenge for the next generation of autonomous robots is the reliable and efficient accomplishment of prolonged, complex, and dynamically changing tasks in the real world. One of the most promising approaches to realizing these capabilities is the plan-based approach to robot control. In the plan-based approach, robots produce control actions by generating, maintaining, and executing plans that are tailored for the robots' respective tasks. Plans are robot control programs that a robot can not only execute but also reason about and manipulate. Thus a plan-based controller is able to manage and adapt the robot's intended course of action --- the plan --- while executing it and can thereby better achieve complex and changing goals. The use of plans enables these robots to flexibly interleave complex and interacting tasks, exploit opportunities, quickly plan their courses of action, and, if necessary, revise their intended activities. One of the grand visions in the area of plan-based robot control is the realization of general autonomous robot control programs that can adapt themselves to the environments they are to operate in and to the distribution of complex tasks they are to perform. An instance of this grand vision is a pre-programmed household robot that knows how to clean a kitchen, how to operate a dishwasher, and so on. Being installed in a new environment it specializes its general plans to the specifics of the household and learns to manage the specific agenda of household chorus that is given to it. The robot also has to learn about the pitfalls of its tasks and its environment and avoid them through foresight. Our research field is still far away from realizing such competent robotic agents.