Talk by Dr. Sylvain Calinon on "Robot Learning from few Samples by Exploiting the Structure and Geometry of Data"
We are delighted to announce that Dr. Sylvain Calinon will be our invited speaker on the fascinating topic of Robot Learning. The talk is scheduled for December 4th and will be part of our Doctoral Seminar.
Dr. Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). His research interests encompass robot learning, optimal control, geometrical approaches, and human-robot collaboration. We can't wait to gain insights from his expertise in Robot Learning and eagerly anticipate your presence!
Today's developments in machine learning heavily focus on big data approaches. However, many applications in robotics require interactive learning approaches that can rely on only few demonstrations or trials. The main challenge boils down to finding structures that can be used in a wide range of tasks, which requires us to advance on several fronts, including (from low level to high level): data structures, geometric structures and combination structures, which I will discuss in my presentation.
As example of data structures, I will discuss the use of tensor factorization techniques that can be used in global optimization problems to efficiently extract and compress information, while providing diverse human-guided learning capabilities (imitation and environment scaffolding). As examples of geometric structures, I will discuss the use of Riemannian geometry and geometric algebra in robotics, where prior knowledge about the physical world can be embedded within the representations of skills and associated learning algorithms. I will then provide a brief overview of combination structures, which relate to movement primitive and behavior primitive representations in robotics, that can be embedded within optimal control problem formulations.
December 4th, 2023, 11 am - 12 am in room 2026, Karlstr. 45.