Daniel Hettegger

Daniel Hettegger is a current Assistant Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at the Technical University of Munich (TUM). His academic journey includes a Master of Science (M.Sc.) in Robotics, Intelligence, Cognition from TUM (2019–2022) and a Bachelor of Science (B.Sc.) in Information and Computer Engineering from the Technical University of Graz (2016–2019).

• Master of Science (M.Sc.) in Robotics, Intelligence, Cognition, Technical University of Munich (2019–2022)
• Bachelor of Science (B.Sc.) in Information and Computer Engineering, Technical University of Graz (2016–2019)

Daniel's research focuses on advanced AI methodologies, including:

• Deep Reinforcement Learning (DRL) for Partially Observable Markov Decision Processes (POMDPs)
• Transformers and Graph Neural Networks (GNNs) in AI systems
• Artificial General Intelligence (AGI) development frameworks

His publications highlight applications of DRL in industrial maintenance and sensor evaluation for robotics. Key trends include:

• Optimization of robotic systems through belief-state-free DRL models
• Development of standardized metrics for depth sensor performance in precision robotics
• Integration of transformer architectures with reinforcement learning frameworks
• Applications of graph neural networks in real-time systems
• Advancing AGI through contextualized robotics problems
• Industrial deployment of AI-driven maintenance solutions

Contact: daniel.hettegger@tum.de