PR2 Beta Sites: Spotlight on TU München

PR2 ThinkerCRAM: Cognitive Robot Abstract Machine

For the PR2 to perform household chores and everyday manipulation tasks, the robot must possess, among other competences, the ability to learn, reason, and plan. Even a seemingly simple task, such as picking up a cup from a table, requires complex and informed decision making. To pick up a cup, the robot must decide where to stand, which hand to use, how to reach, where to grasp, how to lift the cup, and so on. The robot's decision making competencies must be robust enough to take into account not only the task to be performed, but also the situational context in which the task is carried out. For example, were the robot tasked to fill a glass, it would likely grasp the body of the bottle, but were the robot tasked to set the bottle on the floor, a grasp position at the top of the bottle might be more appropriate.

The Technische Universität München (TUM) team develops novel ways of enabling robots to infer the right decisions by considering the relevant aspects of action selection and parametrization.  This means that programmers do not need to specify each single task execution decision in advance.  To this end, the group investigates what they call “cognition-enabled everyday manipulation” or “cognition-enabled perception-action loops”. The research group in Munich interprets cognition to be a resource for better performance: the robot acquires models of the consequences of its actions, and uses these models to select the actions that will best accomplish the robot's objectives.

The main scientific goal of TUM's project is to build CRAM (Cognitive Robot Abstract Machine) as a software toolbox for the design, implementation, and deployment of cognition-enabled autonomous robots performing everyday manipulation activities, such as the PR2. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions, such as those listed above, thereby obviating the need for pre-programmed decisions. In this way, CRAM-programmed autonomous robots become more flexible, reliable, and general-purpose than robots using control programs that lack such reasoning capabilities.


TUM TeamThe Team

The TU München team is an interdisciplinary group of researchers who are members of the German national Cluster of Excellence COTESYS (Cognition for Technical Systems).  Prof. Michael Beetz, who is the head of the Intelligent Autonomous Systems group and vice coordinator of the German Cluster of Excellence COTESYS is leading the overall project and the software development.

The other principal investigators are:

  • Prof. Gordon Cheng:  Humanoid robotics and cognitive systems.
  • Prof. Matthias Kranz: Ubiquitous computing and smart and cognitive objects.
  • Dr. Uwe Haass: General Manager of the Cluster of Excellence COTESYS.

The team includes a group of excellent doctoral students, including the following:

  • Dejan Pangercic: Knowledge-enabled Perception
  • Moritz Tenorth: Knowledge Processing for Mobile Robots and Human Activity Interpretation
  • Nico Blodow: 3D Robotics Perception
  • Mihai Dolha: Human-Robot Interaction and Robot Simulators
  • Dominik Jain: Statistical Relational Learning, Probabilistic Reasoning
  • Ingo Kresse: Robotic Manipulation and Representation of Manipulation Tasks
  • Lars Kunze: Common-sense and Naive Physics Reasoning
  • Alexis Maldonado: Arm/Hand Control for Robotic Manipulation
  • Zoltan-Csaba Marton: 3D Model Fitting, Semantic Labeling of Objects, Arrangements and Environments, Spatio-Temporal Learning
  • Lorenz Mösenlechner: High-Level Planning and Reasoning
  • Federico Ruiz-Ugalde: Connection of Low Level Motor Control with High Level Reasoning and Planning using Object Control
  • Andreas Holzbach: Human Vision
  • Ewald Lutscher: Unified Control Strategies for Robots
  • Marcia Riley: Humanoid Robot Behaviors


Below is a video of Dejan Pangercic presenting TUM's proposal to the rest of the PR2 Beta Program participants. You can download the slides as PDF.

Article written with assistance from Dejan Pangercic.