Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback

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 manipulato...

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Bibliographic Details
Published in:IEEE robotics & automation magazine 2020-06, Vol.27 (2), p.46-54
Main Authors: Perez-Dattari, Rodrigo, Celemin, Carlos, Franzese, Giovanni, Ruiz-del-Solar, Javier, Kober, Jens
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer architecture
Factories
Feature extraction
Industrial plants
Interactive control
Interactive learning
Robot arms
Robots
Service robots
Task analysis
Training
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Summary: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
ISSN:1070-9932
1558-223X
DOI:10.1109/MRA.2020.2983649