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

Perez-Dattari, Rodrigo; Franzese, Giovanni; Kober, Jens

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

Más información

Título según WOS: Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback
Título de la Revista: IEEE ROBOTICS & AUTOMATION MAGAZINE
Volumen: 27
Número: 2
Editorial: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Fecha de publicación: 2020
Página de inicio: 46
Página final: 54
DOI:

10.1109/MRA.2020.2983649

Notas: ISI