The uses of robots are changing from static environments in factories to encompass novel
concepts such as human–robot collaboration in unstructured settings. Preprogramming all …

We present a new Riemannian metric, termed Log-Cholesky metric, on the manifold of
symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a …

This article presents an overview of robot learning and adaptive control applications that can
benefit from a joint use of Riemannian geometry and probabilistic representations. The roles …

We propose a structured prediction approach for robot imitation learning from
demonstrations. Among various tools for robot imitation learning, supervised learning has …

With the ever increasing number of active satellites in space, the rising demand for larger
formations of small satellites and the commercialization of the space industry (so-called New …

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 …

In this paper, we focus on generating complex robotic trajectories by merging sequential
motion primitives. A robotic trajectory is a time series of positions and orientations ending at …

Learning from demonstration (LfD) has been considered an efficient way for skill transfer
from a human user to a robot, to quickly program the robot to perform tasks. Current methods …

Teleoperation in domains such as deep-sea or space often requires the completion of a set
of recurrent tasks. We present a framework that uses a probabilistic approach to learn from …

As a promising branch of robotics, imitation learning emerges as an important way to
transfer human skills to robots, where human demonstrations represented in Cartesian or …