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CoTeSys: Cognition for Technical Systems

How must technical systems be equipped to have abilities similar to those of humans, in order to learn from observations and perceive their environment? How can a robot interact with persons and objects and adapt to unknown situations?

These challenging scientific questions are investigated in CoTeSys. The Cluster of Excellence coordinated by Technische Universität München is a close collaboration between scientists from various disciplines connecting neurocognitive and neuro-biological foundations to engineering sciences at leading research institutions in Munich: besides Technische Universität München, scientists from Ludwig-Maximilians-Universität München, Universität der Bundeswehr, Max-Planck Institute of Neurobiology and German Aerospace Agency DLR are involved.

CoTeSys investigates cognition for technical systems such as vehicles, robots, and factories. Cognitive technical systems are equipped with artificial sensors and actuators, integrated and embedded into physical systems, and act in a physical world. They differ from other technical systems in that they perform cognitive control and have cognitive capabilities. By cognitive capabilities we mean information processing algorithms such as perception, attention, memory, action, learning, and planning. The aim of CoTeSys is to get technical systems that "know what they are doing" - so to speak.

To learn more, please visit the CoTeSys homepage.

Research projects of ours that were (partly) funded by CoTeSys

  • CRAM: Cognitive Robot Abstract Machine
  • PARA: Planning for Adaptive Robot Assistance
  • Cogito: Plan-based control of robotic agents
  • KnowRob: Knowledge processing for autonomous robots
  • ProbCog: Probabilistic Cognition for Technical Systems
  • CogMan: Cognitive robot manipulation
  • Cop: Perception system for robots
  • Multi joint vision: Real-time distributed computer vision with application to human-robot interaction
research/cotesys.txt · Last modified: 2011/11/14 01:45 by jain