Planning for Learning Object Properties

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.

Authors

• Leonardo Lamanna Fondazione Bruno Kessler University of Brescia
• Luciano Serafini
• Mohamadreza Faridghasemnia University of Örebro
• Alessandro Saffiotti University of Örebro
• Alessandro Saetti University of Brescia
• Alfonso Gerevini University of Brescia
• Paolo Traverso Fondazione Bruno Kessler

Keywords

• KRR: Knowledge Acquisition
• PRS: Applications
• PRS: Planning/Scheduling and Learning
• ROB: Cognitive Robotics