Lecturer: Tim Tiedemann
Fields: Robotics, Machine Learning
Content
This course will try to build a bridge between machine learning and robotics.
– Session 1 will give an introduction to robotics, including robotic software frameworks and potential starting points for own robotic experiments
– Session 2 will continue on bio-robotics (showing how biology gained new insights from robotics) and introduce (non-robotic) active learning
– Session 3 will combine both, robotics and active learning. We will also talk about the idea of embodiment.
Literature
- Siciliano et al. (Eds.) (2016, 2024): Handbook of Robotics. Springer
Lecturer
Since 2016: Professor of Intelligent Sensing at the University of Applied Sciences Hamburg (HAW Hamburg, Hamburg, Germany). Main research interests are sensors and sensor data processing (including machine learning methods) and robotics. 2010-2016 Postdoc in the area of space and underwater robotics at the German Research Center for Artificial Intelligence (DFKI) in Bremen, Germany. 2009 Ph.D. in biorobotics, focus on the transfer of (neuro-) biological concepts to the robotic domain 2003-2010 Research assistant at the Computer Engineering Group, Bielefeld University 2003 Research assistant at the Cognitive Psychology Group, Bielefeld University 2003 Diploma in computer science (i.e. Master degree level), study computer science with a focus on robotics and neural networks at Bielefeld University, Bielefeld, Germany
Affiliation: HAW Hamburg