Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

We consider an imitation learning approach to model robot point-to-point (also known as
discrete or reaching) movements with a set of autonomous Dynamical Systems (DS). Each …

The generation of complex movement patterns, in particular, in cases where one needs to
smoothly and accurately join trajectories in a dynamic way, is an important problem in …

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 …

When describing robot motion with dynamic movement primitives (DMPs), goal (trajectory
endpoint), shape and temporal scaling parameters are used. In reinforcement learning with …

An algorithm for learning the dynamics of point-to-point motions from demonstrations using
an autonomous nonlinear dynamical system, named contracting dynamical system …

Temporal abstraction and task decomposition drastically reduce the search space for
planning and control, and are fundamental to making complex tasks amenable to learning …

In this thesis, we discuss approaches that allow robots to learn motor skills. Motor skills can
often be represented by motor primitives, which encode elemental motions. To date, there …

General-purpose autonomous robots must have the ability to combine the available
sensorimotor knowledge in order to solve more complex tasks. Such knowledge is often …