2020
- Automatic Synthesis of Human Motion from Temporal Logic Specifications. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020 mehr… BibTeX Volltext ( DOI ) Volltext (mediaTUM)
Matthias Mayer, M.Sc.
Matthias Mayer is currently a research assistant and PhD student under the supervision of Prof. Dr.-Ing. Matthias Althoff and started in 2020. He holds a bachelor's degree in Engineering Science and a master's degree in Robotics, Cognition, Intelligence both from the Technical University of Munich (TUM). Furthermore, he attended the Georgia Institute of Technology in the fall of 2018.
Matthias Mayer, M.Sc.
House Mail: Boltzmannstraße 3
Technische Universität München
- Tel.: +49 (89) 289 - 18114
- Sprechstunde: On request
- matthias.mayer@tum.de
Research Interests
Matthias Mayer is a Ph.D. student under Prog. Althoff working on modular robots with regards to:
- Configuration optimization considering energy usage, cycle-time, mechanical wear, etc.
- Building a benchmark for manipulator performance which allows to quantitatively compare conventional and modular robots
- Augmenting the kinematics of modular robots, e.g., with prismatic joints or parallel chains
Offered Thesis and HiWi Jobs
If you want to work in the field of modular robotics, simplified robot programming, or task optimized robot selection and have a background in Informatics, RCI, Mechanical, or Electrical Engineering I am eager to meet you and evaluate possible cooperations. Currently, my research interest is mainly focused on the topics proposed below but we can also discuss how your ideas fit into this research.
- [MA/SA/BA] Efficient Path Planning for Modular Robots
- [MA/SA/BA] Solving Real-world Robotics Tasks within 3D Scans
- [MA/SA] Modular Robot Optimization with Machine Learning
Ongoing
- [MA/SA/BA] Robotic Path-Planning for Computer-Aided Manufacturing
- [MA/SA/BA] Optimization of Robot Placement
Theses
| Master Thesis | Visuo-Tactile Exploration of Unknown Environments |
| Bachelor Thesis | An Experimental Evaluation of Transfer Learning in Deep Neural Networks for Car Detection |