We consider kernel methods on general geodesic metric spaces and provide both negative
and positive results. First we show that the common Gaussian kernel can only be …

In this paper, we consider the problem of domain adaptation. We propose to view the data
through the lens of covariance matrices and present a method for domain adaptation using …

In imitation learning, multivariate Gaussians are widely used to encode robot behaviors.
Such approaches do not provide the ability to properly represent end-effector orientation, as …

Body posture influences human and robot performance in manipulation tasks, as
appropriate poses facilitate motion or the exertion of force along different axes. In robotics …

Objective. This paper presents a novel domain adaptation (DA) framework to enhance the
accuracy of electroencephalography (EEG)-based auditory attention classification …

Posture body variation is one of the ways in which humans skillfully and naturally augment
their motion and strength capabilities along specific task-space directions in order to …

Directional data, naturally represented as points on the unit sphere, appear in many
applications. However, unlike the case of Euclidean data, flexible mixture models on the …

Euclidean statistics are often generalized to Riemannian manifolds by replacing straight-line
interpolations with geodesic ones. While these Riemannian models are familiar-looking …

We propose novel finite-dimensional spaces of well-behaved transformations. The latter are
obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity …

Parallel transport is a fundamental tool to perform statistics on Riemannian manifolds. Since
closed formulae do not exist in general, practitioners often have to resort to numerical …