Interactive Learning of Temporal Features for Control

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

Pérez-Dattari, Rodrigo (TU Delft Learning & Autonomous Control)
Celemin, Carlos (TU Delft Learning & Autonomous Control)
Franzese, G. (TU Delft Learning & Autonomous Control)
Ruiz-del-Solar, Javier (Universidad de Chile)
Kober, J. (TU Delft Learning & Autonomous Control)

2020

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.

Artificial neural networks
Service robots
Task analysis
Feature extraction
Training
Computer architecture

http://resolver.tudelft.nl/uuid:26fc3d86-62e5-4dc9-8cac-5cd2c7e8536a

https://doi.org/10.1109/MRA.2020.2983649

2020-10-23

1070-9932

IEEE Robotics and Automation Magazine, 27 (2), 46-54

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2020 Rodrigo Pérez-Dattari, Carlos Celemin, G. Franzese, Javier Ruiz-del-Solar, J. Kober