Advancements in multi-agent, autonomous, and intelligent robotic systems over the past
decades point toward new design and fabrication possibilities. Exploring how humans and …

We present a framework that allows a robot manipulator to learn how to execute structured
tasks from human demonstrations. The proposed system combines physical human–robot …

Human awareness in robot motion planning is crucial for seamless interaction with humans.
Many existing techniques slow down, stop, or change the robot's trajectory locally to avoid …

Stable dynamical systems are a flexible tool to plan robotic motions in real-time. In the
robotic literature, dynamical system motions are typically planned without considering …

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 …

In this paper, we propose an approach to learn stable dynamical systems that evolve on
Riemannian manifolds. Our approach leverages a data-efficient procedure to learn a …

In order to operate close to non-experts, future robots require both an intuitive form of
instruction accessible to laymen and the ability to react appropriately to a human co-worker …

This paper discusses the learning of robot point-to-point motions via non-linear dynamical
systems and Gaussian Mixture Regression (GMR). The novelty of the proposed approach …

Non-linear dynamical systems represent a compact, flexible, and robust tool for reactive
motion generation. The effectiveness of dynamical systems relies on their ability to …

Nowadays, industrial robotics requires that robots and humans share the same workspace
and collaborate to a certain extent. In such a scenario, the safety is the minimum …