Planning for Learning Object Properties

Leonardo Lamanna; Luciano Serafini; Mohamadreza Faridghasemnia; Alessandro Saffiotti; Alessandro Saetti; Alfonso Gerevini; Paolo Traverso

arXiv · 2301.06054

Planning for Learning Object Properties

Leonardo Lamanna·Luciano Serafini·Mohamadreza Faridghasemnia·Alessandro Saffiotti·Alessandro Saetti·Alfonso Gerevini·Paolo Traverso

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Abstract

Autonomous agents embedded in a physical environment need the ability to recognize objects and their properties from sensory data. Such a perceptual ability is often implemented by supervised machine learning models, which are pre-trained using a set of labelled data. In real-world, open-ended deployments, however, it is unrealistic to assume to have a pre-trained model for all possible environments. Therefore, agents need to dynamically learn/adapt/extend their perceptual abilities online, in an autonomous way, by exploring and interacting with the environment where they operate. This paper describes a way to do so, by exploiting symbolic planning. Specifically, we formalize the problem of automatically training a neural network to recognize object properties as a symbolic planning problem (using PDDL). We use planning techniques to produce a strategy for automating the training dataset creation and the learning process. Finally, we provide an experimental evaluation in both a simulated and a real environment, which shows that the proposed approach is able to successfully learn how to recognize new object properties.

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Leonardo Lamanna, Luciano Serafini, Mohamadreza Faridghasemnia, Alessandro Saffiotti, Alessandro Saetti, Alfonso Gerevini, Paolo Traverso. 2023-01-15. Planning for Learning Object Properties. https://arxiv.org/abs/2301.06054

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