no code implementations • 14 Sep 2020 • Murat Kirtay, Guido Schillaci, Verena V. Hafner
This study presents a multisensory machine learning architecture for object recognition by employing a novel dataset that was constructed with the iCub robot, which is equipped with three cameras and a depth sensor.
1 code implementation • 29 Jul 2020 • Guido Schillaci, Alejandra Ciria, Bruno Lara
Here, we suggest that the tracking of prediction error dynamics allows an artificial agent to be intrinsically motivated to seek new experiences but constrained to those that generate reducible prediction error. We present an intrinsic motivation architecture that generates behaviors towards self-generated and dynamic goals and that regulates goal selection and the balance between exploitation and exploration through multi-level monitoring of prediction error dynamics.
no code implementations • 19 May 2020 • Guido Schillaci, Luis Miranda, Uwe Schmidt
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation.
no code implementations • 4 Mar 2020 • Murat Kirtay, Ugo Albanese, Lorenzo Vannucci, Guido Schillaci, Cecilia Laschi, Egidio Falotico
This document presents novel datasets, constructed by employing the iCub robot equipped with an additional depth sensor and color camera.
no code implementations • 7 Jan 2020 • Guido Schillaci, Antonio Pico Villalpando, Verena Vanessa Hafner, Peter Hanappe, David Colliaux, Timothée Wintz
This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot.
1 code implementation • 29 Jul 2019 • Luis Miranda, Guido Schillaci
We present an adaptive model architecture to perform online learning on greenhouse models.
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