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 …

A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …

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 …

We describe CST, an online algorithm for constructing skill trees from demonstration
trajectories. CST segments a demonstration trajectory into a chain of component skills …

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the
world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting …

We present a novel method for segmenting demonstrations, recognizing repeated skills, and
generalizing complex tasks from unstructured demonstrations. This method combines many …

Demonstration trajectories collected from a supervisor in teleoperation are widely used for
robot learning, and temporally segmenting the trajectories into shorter, less-variable …

Most manipulation tasks can be decomposed into a sequence of phases, where the robot's
actions have different effects in each phase. The robot can perform actions to transition …

Much recent work in robot learning from demonstration has focused on automatically
segmenting continuous task demonstrations into simpler, reusable primitives. However …

Inverse optimal control is the problem of computing a cost function with respect to which
observed state and input trajectories are optimal. We present a new method of inverse …