Professor
Angela Schoellig
Alexander von Humboldt Professor for Robotics and Artificial Intelligence
“I am excited about robotics, controls and machine learning, and any novel sensing and computing technology that enables us to design robotic systems that interact with the world and with us.”
Administrative Staff
Deniz Sawo
Administrative Officer
Nadine Grünsteidel
Chair Executive Assistant
Postdoctoral Fellows
Haoming Zhang
Mahathi Anand
PhD Students
Martin Schuck
Ralf Römer
Oliver Hausdörfer
Benjamin Bogenberger
Marcel Rath
Niklas Schlüter
Jim Li
Daniel Florea
Haocheng Zhao
Alexander Hoffmann
Former Students of the Lab
- Alexander von Rohr, 2026, “Safe and robust reinforcement learning.”
- SiQi Zhou, 2025, “Semantically safe robot decision-making.”
- Utku Çulha, 2025, “Physically adaptive robots and process automation.”
- Jacopo Panerati, 2022, “Safe learning-based control for robotics and physics-based simulation.”
- Karime Pereida, 2021, “Adaptive and learning controllers for high-accuracy trajectory tracking in changing conditions.”
- Mario Vukosavljev, 2019, “UAV swarms for robust task execution.”
- Mohammad Nahangi, 2019, “Autonomous UAV monitoring of the construction progress in indoor environments.” Coadvised with Brenda McCabe.
- Michael Warren, 2019, “Vision-based navigation of UAVs.” Coadvised with Tim Barfoot.
- Mohamed Helwa, 2018, “Control design for safety-critical systems; safe, online learning of robots; transfer learning in robotics.”
- Zachary Kroeze, 2018, “Localization, mapping, and control of an autonomous vehicle for the SAE AutoDrive Challenge.”
- Adam Heins, 2025, “Robust mobile manipulation for robotic pushing and nonprehensile object transportation.”
- Keenan Burnett, 2025, “Towards radar-based mapping and localization in all weather conditions.” Coadvised with Tim Barfoot.
- Wenda Zhao, 2024, “Accurate and robust ultra-wideband time difference of arrival localization for multi-agent systems.”
- Melissa Greeff, 2022, “Flying flat out: fast multirotor flight using vision-based navigation in real-world environments.”
- SiQi Zhou, 2022, “Neural networks as add-on modules for improved performance of robot control systems.”
- Thomas Bamford, 2021, “Application of unmanned aerial systems to blast monitoring in open pit mines.” Coadvised with Kamran Esmaeili.
- Chris McKinnon, 2021, “Reliable path-tracking control for ground robots in changing conditions.”
- Karime Pereida, 2020, “Adaptive and learning controllers for high-accuracy trajectory tracking in changing conditions.”
- Felix Berkenkamp, 2019, “Safe exploration in reinforcement learning: theory and applications in robotics.” Coadvised with Andreas Krause.
- Mario Vukosavljev, 2019, “Modular framework for motion planning based on feedback-based motion primitives.” Coadvised with Mireille Broucke.
- Chris Ostafew, 2016, “Learning control for vision-based mobile robot path tracking in outdoor environments.” Coadvised with Tim Barfoot.
- Tsung-Yuan Tseng, 2025, “Efficient risk-averse Bayesian optimization for robot learning.”
- Mingxuan Che, 2025, “Quantifying prior knowledge in learning-based control and reinforcement learning.”
- Shambhuraj Sawant, 2024, “Safe reinforcement learning and model predictive control.”
- Veronica Chatrath, 2023, “Probabilistic object-level change detection and localization.”
- Juergen Scherer, 2019, “Multi-robot coordination for surveillance missions.”
- Andriy Sarabakha, 2018, “Online adaptation with deep neural networks for high-performance autonomous flight.”
- Tsung-Yuan Tseng, 2023, “HPO for Learning-Based Control and RL.”
- Ralf Römer, 2023, “The Role of Control Frequency for Uncertain Systems.”
- Nicoló Musolesi, 2024, “Multiagent Planning and Control.”
- Ben Sprenger, 2024, “Multiagent Behaviour Learning.”
- Tim Emmert, 2024, “Drone Racing with Moving Gates.”
- Christopher Biel, 2024, “System Identification.”
- Mingxuan Che, 2024, “Benchmarking for Learning-Based Control and RL.”
- Jonathan Kelm, 2024, “Approximate MPC with Diffusion Models.”
- Adrian Kobras, 2024, “Autonomy through Deployment.”
- Yanni Zhang, 2024, “Semantically Safe Perception-Based Control.”
- Yi Zhang, 2024, “Lifelong Learning with Diffusion Policies.”
- Tobias Farger, 2025, “Efficient Gaussian Process MPC using Differential Flatness.”
- Marcel Rath, 2025, “Efficient & Less Conservative Robust Adaptive MPC.”
- Niklas Schlüter, 2025, “HiWi, Drone Racing.”
- Luca Worbis, 2025, “Continual Learning with Vision-Language-Action Models.”
- Haocheng Zhao, 2025, “Superquadrics for Perception-Based Semantic Safety in Dynamic Scenes.”
- Jiaming Zhang, 2026, “HiWi drone racing.”