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 …

This paper presents a method to learn discrete robot motions from a set of demonstrations.
We model a motion as a nonlinear autonomous (ie, time-invariant) dynamical system (DS) …

Robot PbD started about 30 years ago, growing importantly during the past decade. The
rationale for moving from purely preprogrammed robots to very flexible user-based …

We presented and evaluated an approach based on HMM, GMR, and dynamical systems to
allow robots to acquire new skills by imitation. Using HMM allowed us to get rid of the explicit …

Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming
by Demonstration (PbD) develops methods by which new skills can be transmitted to a …

We present a probabilistic architecture for solving generically the problem of extracting the
task constraints through a programming by demonstration framework and for generalizing …

We present a probabilistic architecture for solving generically the problem of extracting the
task constraints through a programming by demonstration (PbD) framework and for …

Abstract Human-Robot Interaction and especially Human-Robot Communication (HRC) is of
primary importance for the development of robots that operate outside production lines and …

Gaussian Mixture Regression has been shown to be a powerful and easy-to-tune regression
technique for imitation learning of constrained motor tasks in robots. Yet, current …

We consider the problem of learning robust models of robot motion through demonstration.
An approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression …