In the context of robotics and automation, learning from demonstration (LfD) is the paradigm
in which robots acquire new skills by learning to imitate an expert. The choice of LfD over …

Robots that physically interact with their surroundings, in order to accomplish some tasks or
assist humans in their activities, require to exploit contact forces in a safe and proficient …

In human-robot collaborative assembly, robots are often required to dynamically change
their pre-planned tasks to collaborate with human operators in a shared workspace …

In this survey, we present the current status on robots performing manipulation tasks that
require varying contact with the environment, such that the robot must either implicitly or …

Ensuring human safety is one of the most important considerations within the field of human-
robot interaction (HRI). This does not simply involve preventing collisions between humans …

Reinforcement Learning (RL) of contact-rich manipulation tasks has yielded impressive
results in recent years. While many studies in RL focus on varying the observation space or …

Robots are becoming safe and smart enough to work alongside people not only on
manufacturing production lines, but also in spaces such as houses, museums, or hospitals …

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

In robotics, the ultimate goal of reinforcement learning is to endow robots with the ability to
learn, improve, adapt and reproduce tasks with dynamically changing constraints based on …

Impedance control is a commonly used control architecture for robotic manipulation. For
increased flexibility, the impedance can be programmed to vary during the task. This has …