Planning for Learning Object Properties


  • Leonardo Lamanna Fondazione Bruno Kessler University of Brescia
  • Luciano Serafini Fondazione Bruno Kessler
  • 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



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


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.




How to Cite

Lamanna, L., Serafini, L., Faridghasemnia, M., Saffiotti, A., Saetti, A., Gerevini, A., & Traverso, P. (2023). Planning for Learning Object Properties. Proceedings of the AAAI Conference on Artificial Intelligence, 37(10), 12005-12013.



AAAI Technical Track on Planning, Routing, and Scheduling