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I am interested in autonomous robots using different kind of knowledge, in particular learned models, to show adaptive, robust behavior. With learned models the robot has expectations about the future evolution of the world and its own behavior. This knowledge is valuable for robust and flexible plan execution and failure monitoring. In particular, prediction models of human behavior are an important prerequisite for natural, flexible and safe human-robot cooperation.

Flexible and adaptive robot behavior is most valuable in human-robot cooperative scenarios. I'm especially interested in human-robot scenarios in the context of assistance and eldercare, where it is not necessary that the robot perform all its tasks on its own, but where it provides dedicated assistance where people need it and in return can be assisted by people in tasks it cannot perform by its own means.

I lead an independent junior research group funded by the cluster of excellence Cognition for Technical Systems with the title "Planning for Adaptive Robot Assistance". Since April 2010 I am a Carl-von-Linde Junior Fellow at the Institute for Advanced Study of TUM.

Research Topics

  • Automated Learning
  • Plan-Based Robot Control
  • Model-Based Reasoning
  • Transformational Planning
  • Failure Diagnosis

Current Research Projects

  • PARA : This projects develops plan-based control mechanisms for human-robot interaction, where the robot assists the person in everyday tasks and adapts to the person's abilities, expectations and preferences. The joint human-robot plan is represented explicitly in the robot program. To opimize the joint execution, we develop methods for representing and learning models of a person's capabilities, expectations and preferences. We apply this research in different domains, especially in the context of elderly care, where a robot assistant can enable elderly people to live independent, who currently depend on the help of others.

Former Research Projects

  • 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.
  • Agilo : Robotic soccer has become a standard 'real-world' testbed for autonomous multi robot control. In robot soccer (mid-size league) two teams of four autonomous robots --- one goal keeper and three field players --- play soccer against each other. The soccer field is four by nine meters big. The key characteristics of mid-size robot soccer are that the robots are completely autonomous. Consequently, all sensing and all action selection is done onboard of the individual robots. Skillful play requires our robots to recognize objects, such as other robots, field lines, and goals, and even entire game situations. In the AGILO project we investigate how probabilistic visuomotoric autonomous robot controllers that are capable of learning can meet these challenges. The AGILO robot controllers employ game state estimation and situated action selection based on automatically learned control mechanisms.

Teaching General

Student Projects

Courses

Selected Publications

  • Alexandra Kirsch , Robot Learning Language --- Integrating Programming and Learning for Cognitive Systems, 2009, Robotics and Autonomous Systems Journal,
    (BibTeX) (PDF)
  • Alexandra Kirsch , Integration of Programming and Learning in a Control Language for Autonomous Robots Performing Everyday Activities, 2008,
    (BibTeX) (PDF)
  • Alexandra Kirsch , Michael Beetz , Training on the Job --- Collecting Experience with Hierarchical Hybrid Automata, 2007, Proceedings of the 30th German Conference on Artificial Intelligence (KI-2007),
    (BibTeX) (PDF)
  • Alexandra Kirsch , Michael Schweitzer, Michael Beetz , Making Robot Learning Controllable: A Case Study in Robot Navigation, 2005, Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check,
    (BibTeX) (PDF)
For the full list of my Publications please see the Publications section.
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