Scholar
Rudolf Reiter
Google Scholar ID: 5VdYugYAAAAJ
Postdoctoral Researcher at University of Zurich
Citations & Impact
Citations
146
H-index
7
i10-index
7
Publications
20
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- Published multiple papers, including 'Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification' (2025, arXiv.org), PhD thesis 'Thesis: Optimization-based motion planning and obstacle avoidance for autonomous driving and racing' (2024, University of Freiburg), and 'AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control' (2024, arXiv.org).
Research Experience
- During his PhD (2021-2024) at the University of Freiburg, used numerical and combinatorial optimization techniques, aided by machine learning methods, to address the computational demands of autonomous driving. Collaborated with leading research institutions and industry labs, including IMT Lucca (under Prof. Dr. Alberto Bemporad), Mitsubishi Electric Research Laboratories (Cambridge, MA), and ETH Zürich (with Prof. Dr. Melanie Zeilinger). Prior to his PhD, worked as a control systems specialist at Anton Paar GmbH (2016-2018) and as an applied scientist at the Virtual Vehicle Research Center (2018-2021). Also, was a member of the Autonomous Racing Graz team from 2021 to 2024, competing in real-world autonomous racing challenges.
Education
- Master's degree in Electrical Engineering (with a focus on control systems) from Graz University of Technology, Austria, in 2016. PhD from the University of Freiburg between 2021 and 2024, with a thesis titled “Optimization-Based Motion Planning and Obstacle Avoidance for Autonomous Driving and Racing,” supervised by Prof. Dr. Moritz Diehl and funded by the Marie-Skłodowska Curie Innovative Training Network.
Background
- Research interests: advanced controls, optimization, machine learning, and robotics. Current work in the Robotics and Perception Group (led by Prof. Davide Scaramuzza) in Zurich, Switzerland, and the Systems Control and Optimization Laboratory (with Prof. Moritz Diehl) at the University of Freiburg, Germany. Research focus is on learning- and optimization-based motion planning and control, particularly model-based reinforcement learning approaches that embed predictive planners into RL policies.
