The next Doctoral Research Seminar will present "Robot learning from few samples by exploiting the structure and geometry of data " by Sylvain Calinon on Tuesday.
February 13th, 2023, 11:00 am - 12:00 am

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
#machinelearning #robotics #learningalgorithms