Interactive Learning of Temporal Features for Control

Shaping Policies and State Representations From Human Feedback

Journal article (2020)

Authors

Rodrigo Pérez-Dattari Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Carlos Celemin Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
G. Franzese Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Javier Ruiz-del-Solar Universidad de Chile
J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 R.J. Perez Dattari, Carlos Celemin, G. Franzese, Javier Ruiz-del-Solar, J. Kober
DOI
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https://doi.org/10.1109/MRA.2020.2983649
Feature extraction Computer architecture Service robots Training Artificial neural networks Task analysis
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

Abstract

Current ongoing industry revolution demands more flexible products, including robots in household environments and medium-scale factories. Such robots should be able to adapt to new conditions and environments and be programmed with ease. As an example, let us suppose that there are robot manipulators working on an industrial production line and that they need to perform a new task. If these robots were hard coded, it could take days to adapt them to the new settings, which would stop production at the factory. Robots that non-expert humans could easily program would speed up the process considerably.