I focus on integrating, into robots, as much formally represented domain knowledge as possible especially for realtime algorithms and other software close to the hardware, the controller(s) and the sensor(s). In other words, I am on a continuous quest for the holy grail of the (mythical) “robotics ontology”…
This puts my research in the (currently sparsely populated) corner of Artificial Intelligence that is opposite to the (currently extremely popular) (deep) learning. My knowledge representations do include the relations with which the latter techniques can be integrated into any robotic system, in a systematic way. (Like any other AI technique for that matter.) That integration consists of how to configure the many “magic numbers” that the (so-called) “model-less” techniques require for a proper working in a specific task and application context; this configuration in itself requires quite some understanding of the intriciate dependencies between the perception, plans, control and monitoring activities in a robotic system.
My research takes place in close cooperation with Erwin Aertbeliën (super expert in dissecting and programming the most difficult robot tasks and in numerical solvers), Wilm Decré (my main liason with industrial projects), Joris De Schutter (former supervisor, co-creator of most of my “robot skills” R&D, reads robotics challenges as no-one else), Goele Pipeleers (for solvers of constrained optimization problems), Jan Swevers (main liason for all things control), René van de Molengraft (core co-creator of my approach towards system-of-systems architectures, and especially to “lazy” robot skills), Eric Demeester (main liason for shared control and industrial robot vision), Peter Slaets (main liason for unmanned (“autonomous”) shipping), Mark Versteyhe (main liason to the mechatronics industry in Flanders), and a manageably small set of PhD students and postdocs. All of the KU Leuven people have enjoyed the view from standing on the shoulders of our robotics founding father Rik van Brussel.