• ML for autonomous systems & robots
• Methodical focus on RL
• Reinforcement Learning
• AI-supported decision making
• Intelligent transportation systems and aviation
• Human-AI interaction and co-learning
• Embodied AI
• Multi-agent systems

Our group boasts a wide range of expertise, given the diverse backgrounds of our team members. We develop AI systems which have a positive influence on society and benefit humans, assist in complex environments, automate processes and push the frontier towards collective intelligence. Our focus lies on the development of AI approaches inspired by biological and feedback learning, hence the name “cybernetics”. David A. Mindell defined cybernetics as “the study of human/machine interaction”, whereby he states the core idea that most any system can be analyzed using the principles of feedback, communication and control, which we emulate in our research. Developing holistic approaches to intricate problems requires the usage of more than one tool, which is why we leverage the best suited approaches, ranging from multi-agent, distributed and hierarchical to causal RL. Our AI-agents should sense and interact with both their environment and other human and artificial agents within it, while behaving in a predictable and reliable manner. Our fields of applicability are virtually unlimited; integrating AI into transport systems such as aviation and rail, supporting human decision-makers, fully automating processes and robotics, to name a few. It is essential to us to maintain human agency and control over systems, ensuring that human domain knowledge and expertise is not lost or replaced, but supported and enhanced. 

Also see: https://cyberneticlearningsystems.github.io/