Matteo Leonetti

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For mobile robots to perform complex missions, it may be necessary for them to plan with incomplete information and reason about the indirect effects of their actions. Answer Set Programming (ASP) provides an elegant way of formalizing domains which involve indirect effects of an action and recursively defined fluents. In this paper, we present an approach(More)
The action language BC provides an elegant way of formalizing dynamic domains which involve indirect effects of actions and recursively defined fluents. In complex robot task planning domains, it may be necessary for robots to plan with incomplete information, and reason about indirect or recursive action effects. In this paper, we demonstrate how BC can be(More)
In a reinforcement learning setting, the goal of transfer learning is to improve performance on a target task by re-using knowledge from one or more source tasks. A key problem in transfer learning is how to choose appropriate source tasks for a given target task. Current approaches typically require that the agent has some experience in the target domain,(More)
Transfer learning in reinforcement learning has been an active area of research over the past decade. In transfer learning, training on a source task is leveraged to speed up or otherwise improve learning on a target task. This paper presents the more ambitious problem of curriculum learning in reinforcement learning, in which the goal is to design a(More)
Batching is a well known technique to boost the throughput of Total Order Broadcast (TOB) protocols. Unfortunately, its manual configuration is not only a time consuming process, but also a very delicate one, as incorrect settings of the batching parameter can lead to severe performance degradation. In this paper we address precisely this issue, by(More)
We investigate methods to improve fault-tolerance of Autonomous Underwater Vehicles (AUVs) to increase their reliability and persistent autonomy. We propose a learning-based approach that is able to discover new control policies to overcome thruster failures as they happen. The proposed approach is a model-based direct policy search that learns on an(More)
We describe the design and implementation of an on-line identification scheme for Autonomous Underwater Vehicles (AUVs). The proposed method estimates the dynamic parameters of the vehicle based on a global derivative-free optimization algorithm. It is not sensitive to initial conditions, unlike other on-line identification schemes, and does not depend on(More)
Agent programming in complex, partially observable, and stochastic domains usually requires a great deal of understanding of both the domain and the task in order to provide the agent with the knowledge necessary to act effectively. While symbolic methods allow the designer to specify declarative knowledge about the domain, the resulting plan can be brittle(More)
Recent progress in both AI and robotics have enabled the development of general purpose robot platforms that are capable of executing a wide variety of complex, temporally extended service tasks in open environments. This article introduces a novel, custom-designed multi-robot platform for research on AI, robotics, and especially human–robot interaction for(More)
We propose a method for computing on-line the controller of an Autonomous Underwater Vehicle under thruster failures. The method is general and can be applied to both redundant and under-actuated AUVs, as it does not rely on the modification of the thruster control matrix. We define an optimization problem on a specific class of functions, in order to(More)