Trustworthy Autonomy

Trustworthy autonomy focuses on developing autonomous systems that are not only capable and efficient but also safe, reliable, and demonstrably trustworthy. Current research emphasizes formal verification methods, such as reachability analysis and control barrier functions, alongside machine learning techniques like reinforcement learning, to ensure safety and predictable behavior in complex environments. This field is crucial for the safe deployment of autonomous systems in various domains, from robotics and autonomous vehicles to multi-agent systems, impacting both the safety of human-robot interaction and the broader acceptance of autonomous technologies.