THA_air

Research Areas

Perception and Sensing

Prof. Dr. Michael Strohmeier

Innovative sensor technologies and perception methods enable Physical AI systems to understand their environment reliably under real-world conditions. The integration of heterogeneous, multimodal sensor data with learning-based semantic representations and uncertainty quantification provides the basis for physically grounded state estimation. ↗

Main Research Area

Control and Safety

Prof. Dr.-Ing. Thomas Kirchmeier

Robust control methods turn decisions into safe, reliable and efficient physical actions. Hybrid neural architectures, risk-based situation classification and systematic uncertainty management help ensure functional safety and fail-operational behavior. ↗

Main Research Area

Explainable Human-Machine Interaction

Prof. Dr. Silvan Mertes

Transparent and trustworthy interaction connects people with intelligent physical systems. Counterfactual explanations and adaptive speech synthesis help communicate decisions as well as the underlying uncertainty, keeping Physical AI understandable and aligned with human intent. ↗

Main Research Area

Artificial Intelligence and Decision-Making

Prof. Dr.-Ing. Honorary Doctor of ONPU Thorsten Schöler

Advanced machine learning, reasoning and planning methods enable Physical AI systems to make reliable decisions in complex and uncertain environments. Neuro-symbolic architectures and digital twins provide the basis for scalable, constraint-aware decision-making in both individual autonomous systems and multi-agent systems. ↗

Main Research Area

Systems and Architectures for Autonomous Systems

Prof. Dr. Constantin Wanninger

Modular hardware and software architectures bring together sensing, communication and actuation in coherent Physical AI platforms. System-of-systems structures with self-organizing subsystems and semantic modeling frameworks help accelerate development and code synthesis. ↗