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Research Topics

  • Plan-Based Robot Control
  • Plan Optimization
  • Transformational Planning
  • RPL

Current Research Projects

  • 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.

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.
For my previously taught courses please see All Terms section.

Selected Publications

  • Lorenz Mösenlechner , Michael Beetz , Using Physics- and Sensor-based Simulation for High-fidelity Temporal Projection of Realistic Robot Behavior, 2009, 19th International Conference on Automated Planning and Scheduling (ICAPS'09).,
    (BibTeX) (PDF)
  • Lorenz Mösenlechner , Armin Müller , Michael Beetz , High Performance Execution of Everyday Pick-and-Place Tasks by Integrating Transformation Planning and Reactive Execution, 2008, Proceedings of the 1st International Workshop on Cognition for Technical Systems, M{\"u}nchen, Germany, 6-8 October,
    (BibTeX)
For the full list of my Publications please see the Publications section.
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