Wouter Kouw develops artificially intelligent systems that learn from interaction with their environment. He focuses on probabilistic machine learning systems where inference and decision-making are implemented as message passing on factor graphs. He derives online system identification algorithms for time-varying dynamical systems and free energy minimizing agents that plan actions to simultaneously learn fast and reach a goal. Much of this work concerns actively pursuing informative data points or trials. Applications include multi-joint dynamical systems, mobile robots, heat transfer processes, traffic management, radar tracking, and neuromorphic computing.

The aim is to bring these agents to legged robot platforms that use contact to resolve perceptual ambiguity and negotiate unknown terrain, with the ultimate goal of automating work that is dangerous or infeasible for humans.

“Intelligence is more than just computation at scale - it’s about making sense of the world under uncertainty.”