Learning to Act for Perceiving in Partially Unknown Environments
Learning to Act for Perceiving in Partially Unknown Environments
Leonardo Lamanna, Mohamadreza Faridghasemnia, Alfonso Gerevini, Alessandro Saetti, Alessandro Saffiotti, Luciano Serafini, Paolo Traverso
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 5485-5493.
https://doi.org/10.24963/ijcai.2023/609
Autonomous agents embedded in a physical environment need the ability to correctly perceive the state of the environment from sensory data. In partially observable environments, certain properties can be perceived only in specific situations and from certain viewpoints that can be reached by the agent by planning and executing actions. For instance, to understand whether a cup is full of coffee, an agent, equipped with a camera, needs to turn on the light and look at the cup from the top. When the proper situations to perceive the desired properties are unknown, an agent needs to learn them and plan to get in such situations. In this paper, we devise a general method to solve this problem by evaluating the confidence of a neural network online and by using symbolic planning. We experimentally evaluate the proposed approach on several synthetic datasets, and show the feasibility of our approach in a real-world scenario that involves noisy perceptions and noisy actions on a real robot.
Keywords:
Robotics: ROB: Cognitive robotics
Planning and Scheduling: PS: Learning in planning and scheduling
Robotics: ROB: Perception