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 …

In this paper, we provide an overview of the existing methods for integrating human advice
into a reinforcement learning process. We first propose a taxonomy of the different forms of …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

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 …

We present a comprehensive survey of robot Learning from Demonstration (LfD), a
technique that develops policies from example state to action map**s. We introduce the …

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 …

In order for robots to learn from people with no machine learning expertise, robots should
learn from natural human instruction. Most machine learning techniques that incorporate …

The main goal of this article is to present COACH (COrrective Advice Communicated by
Humans), a new learning framework that allows non-expert humans to advise an agent …

A goal of interactive machine learning (IML) is to enable people with no specialized training
to intuitively teach intelligent agents how to perform tasks. Toward achieving that goal, we …

In a Human-in-the-Loop paradigm, a robotic agent is able to act mostly autonomously in
solving a task, but can request help from an external expert when needed. However …