Today's developments in machine learning heavily focus on big data
approaches. However, many applications in robotics require 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 data structures and geometric structures.

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.