This chapter surveys the main approaches developed to date to endow robots with the
ability to learn from human guidance. The field is best known as robot programming by …

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 …

Policy search is a subfield in reinforcement learning which focuses on finding good
parameters for a given policy parametrization. It is well suited for robotics as it can cope with …

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 …

Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks
that are strongly structured. Such task structures can be exploited by incorporating …

We present GhostAR, a time-space editor for authoring and acting Human-Robot-
Collaborative (HRC) tasks in-situ. Our system adopts an embodied authoring approach in …

Recent success of machine learning in many domains has been overwhelming, which often
leads to false expectations regarding the capabilities of behavior learning in robotics. In this …

There is a large variety of objects and appliances in human environments, such as stoves,
coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a …

Robot Learning from Demonstration (LfD) or Robot Programming by Demonstration
(PbD)(also known as Imitation Learning and Apprenticeship Learning) is a paradigm for …