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Leonardo Lamanna

5 accepted papers

2023

Learning to Act for Perceiving in Partially Unknown Environments

Leonardo Lamanna, Mohamadreza Faridghasemnia, Alfonso Gerevini, Alessandro Saetti, Alessandro Saffiotti, Luciano Serafini +1
IJCAI 2023poster

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 age…

Cited by 6Source ↗PDF ↗Scholar ↗
2023

Planning for Learning Object Properties

Leonardo Lamanna, Luciano Serafini, Mohamadreza Faridghasemnia, Alessandro Saffiotti, Alessandro Saetti, Alfonso Gerevini +1
AAAI 2023technical

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 deplo…

Cited by 9Source ↗PDF ↗Scholar ↗
2022

Online Learning of Reusable Abstract Models for Object Goal Navigation

Tommaso Campari, Leonardo Lamanna, Paolo Traverso, Luciano Serafini, Lamberto Ballan
CVPR 2022poster

In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state…

Cited by 26PDF ↗Scholar ↗
2021

On-line Learning of Planning Domains from Sensor Data in PAL: Scaling up to Large State Spaces

Leonardo Lamanna, Alfonso Emilio Gerevini, Alessandro Saetti, Luciano Serafini, Paolo Traverso
AAAI 2021technical

We propose an approach to learn an extensional representation of a discrete deterministic planning domain from observations in a continuous space navigated by the agent actions. This is achieved through the use of a perception function providing the likelihood of a real-value observation being in a…

Cited by 14Source ↗PDF ↗Scholar ↗
2021

Online Learning of Action Models for PDDL Planning

Leonardo Lamanna, Alessandro Saetti, Luciano Serafini, Alfonso Gerevini, Paolo Traverso
IJCAI 2021poster

The automated learning of action models is widely recognised as a key and compelling challenge to address the difficulties of the manual specification of planning domains. Most state-of-the-art methods perform this learning offline from an input set of plan traces generated by the execution of (succ…

Cited by 38Source ↗PDF ↗Scholar ↗