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Research area: Autonomous Robots: Robotic Soccer and beyond
Making robots act autonomously in a complex environment bears many challenges: uncertainties in sensor and actors, many degrees of freedom, constraints to be considered, etc.
We therefore consider learning robots to be a highly interesting application domain for machine learning techniques. In particular we are interested in reinforcement learning controllers: the designer only specifies the goal and the actions; the robot then learns the appropriate behavior by (clever) trial and error.
Some concrete research projects within this area are:
- Robotic Soccer: The Brainstormers (since 1998)
- Reinforcement Controllers in Technical Applications (since 1994)
- Learning Algorithms for Cooperative Multi-Agent Systems (DFG; 2004-2006)
- RL in real, distributed, and cooperative environments (DFG SPP 1125; 2001- 2006)
- Reinforcement Learning in Realtime (DFG SFB 531; 2003-2004)
- Learning on Varying Time Scales (DFG; 1998-2000)
- Fynesse: Fuzzy Neuro Systems for Control (DFG; 1996-2000)
- OpenTribot: Design of a standard plattform (hard- and software) for the RoboCup Midsize League in an Open-Source project (DFG RI 923/6-1; 2009-2012)
See also our publications page for further information.
