Learning from demonstration (LfD) has been used to help robots to implement manipulation
tasks autonomously, in particular, to learn manipulation behaviors from observing the motion …

Task-parameterized models of movements aim at automatically adapting movements to new
situations encountered by a robot. The task parameters can, for example, take the form of …

Imitating human demonstrations is a promising approach to endow robots with various
manipulation capabilities. While recent advances have been made in imitation learning and …

We introduce the value iteration network (VIN): a fully differentiable neural network with
aplanning module'embedded within. VINs can learn to plan, and are suitable for predicting …

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) …

Imitation learning is a powerful tool for training robot manipulation policies, allowing them to
learn from expert demonstrations without manual programming or trial-and-error. However …

Recently, there have been considerable advances in the research on innovative information
communication technology (ICT) for the education of people with autism. This review …

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 …

Regression is the process of learning relationships between inputs and continuous outputs
from example data, which enables predictions for novel inputs. The history of regression is …

Acquisition of new sensorimotor knowledge by imitation is a promising paradigm for robot
learning. To be effective, action learning should not be limited to direct replication of …