Von: Dennis Mronga
Learning Task Constraints for Real-Time Collision Avoidance
Robot control based on constrained optimization is a powerful method to control complex robotic tasks based on constraints. The idea is to break down the overall control problem into simultaneously running subtasks and merge them into a coherent robot control signal using constrained optmization. Despite on its capabilities in specifying complex robot control problems, the control solution is usually governed by a rather large set of parameters, which have to be tuned manually. Combining constraint-based robot control with learning approaches might not only simplify the cumbersome process of parameter selection, but also provide novel, context-adaptive control solutions.
In this talk I would like to illustrate my recent progress on context-adaptive robot control based on constraints. For this purpose, I will present an approach for learning task constraints in a real-time human-robot collision avoidance scenario. Experimental results are presented on a dual-arm industrial robot system, which is supposed to avoid collisions with a human operator entering its workspace.
In der Regel sind die Vorträge Teil von Lehrveranstaltungsreihen der Universität Bremen und nicht frei zugänglich. Bei Interesse wird um Rücksprache mit dem Sekretariat unter sek-ric(at)dfki.de gebeten.