From passive to interactive object learning and recognition through self-identification on a humanoid robot

Abstract

Service robots, working in evolving human environments, need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to… (More)
DOI: 10.1007/s10514-015-9445-0

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Cite this paper

@article{Lyubova2016FromPT, title={From passive to interactive object learning and recognition through self-identification on a humanoid robot}, author={Natalia Lyubova and Serena Ivaldi and David Filliat}, journal={Auton. Robots}, year={2016}, volume={40}, pages={33-57} }