As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Biological systems, including human beings, have the innate ability to perform complex
tasks in a versatile and agile manner. Researchers in sensorimotor control have aimed to …

Nonlinear dynamical systems have been used in many disciplines to model complex
behaviors, including biological motor control, robotics, perception, economics, traffic …

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 …

We provide a general approach for learning robotic motor skills from human demonstration.
To represent an observed movement, a non-linear differential equation is learned such that …

One promising approach for robots efficiently learning skills is to learn manipulation skills
from human tutors by demonstration and then generalize these learned skills to complete …

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 …

Manipulation skill learning and generalisation have gained increasing attention due to the
wide applications of robot manipulators and the spurt of robot learning techniques …

A method to learn and reproduce robot force interactions in a human–robot interaction
setting is proposed. The method allows a robotic manipulator to learn to perform tasks that …

We present an approach allowing a robot to acquire new motor skills by learning the
couplings across motor control variables. The demonstrated skill is first encoded in a …