Movement Primitives (MP) are a well-established approach for representing modular and re-
usable robot movement generators. Many state-of-the-art robot learning successes are …

Movement Primitives are a well-established paradigm for modular movement representation
and generation. They provide a data-driven representation of movements and support …

This paper introduces a benchmark framework to evaluate the performance of reaching
motion generation approaches that learn from demonstrated examples. The system …

Movement primitives (MPs) provide a powerful framework for data driven movement
generation that has been successfully applied for learning from demonstrations and robot …

This article describes our open-source software for predicting the intention of a user
physically interacting with the humanoid robot iCub. Our goal is to allow the robot to infer the …

This article proposes a hierarchical learning architecture for safe data-driven control in
unknown environments. We consider a constrained nonlinear dynamical system and …

Movement prioritization is a common approach to combine controllers of different tasks for
redundant robots, where each task is assigned a priority. The priorities of the tasks are often …

Physical interaction requires robots to accurately follow kinematic trajectories while
modulating the interaction forces to accomplish tasks and to be safe to the environment …

Intelligent robots can potentially assist humans in everyday life and industrial production
processes. However, the variety of different tasks for such robots renders pure …

We propose a hierarchical learning architecture for predictive control in unknown
environments. We consider a constrained nonlinear dynamical system and assume the …