Guglielmo Gemignani

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Human Robot Interaction is a key enabling feature to support the introduction of robots in everyday environments. However, robots are currently incapable of building representations of the environments that allow both for the execution of complex tasks and for an easy interaction with the user requesting them. In this paper, we focus on semantic mapping,(More)
While operating in domestic environments, robots will necessarily face difficulties not envisioned by their developers at programming time. Moreover, the tasks to be performed by a robot will often have to be specialized and/or adapted to the needs of specific users and specific environments. Hence, learning how to operate by interacting with the user seems(More)
The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing " knowledgeable " robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the(More)
Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the(More)
Distributed Particle filter-based algorithms have been proven effective tools to model non-linear and dynamic processes in Multi Robot Systems. In complex scenarios, where mobile agents are involved, it is crucial to disseminate reliable beliefs among agents to avoid the degradation of the global estimations.We present a cluster-based data association to(More)
Several research efforts address the challenge of having users incrementally teach or demonstrate a task to a robot. We are interested in an autonomous robot that persists over time and the problem of teaching it an additional task. We believe that the assumption that a user would know all the tasks previously taught to the robot does not hold. We hence(More)
In this work, we consider an autonomous robot that is required to understand commands given by a human through natural language. Specifically, we assume that this robot is provided with an internal representation of the environment. However, such a representation is unknown to the user. In this context, we address the problem of allowing a human to(More)