The increasing diffusion of deepfakes has raised significant global concerns, especially due to their potential geopolitical implications. This concern relates to the spread of false information that can mislead people and have a serious impact on societies. However, identifying what can misinform people is not trivial. In this work, we present an experimental study that involves a sample of students of different backgrounds. Three different political deepfakes were created and shown to them. The perceived values were then analyzed using a specific questionnaire. The experimental results show significant differences in the ability to discern the authenticity of the proposed videos depending on the level of awareness of the contents viewed. This demonstrates the crucial role of education on deepfakes in countering the spread of misinformation.

Recent years have witnessed a growing interest in automating labor-intensive and complex activities, i.e., those consisting of multiple atomic tasks, by deploying robots in dynamic and unpredictable environments such as industrial and agricultural settings. A key characteristic of these contexts is that activities are not predefined: while they involve a limited set of possible tasks, their combinations may vary depending on the situation. Moreover, despite recent advances in robotics, the ability for humans to monitor the progress of high-level activities - in terms of past, present, and future actions - remains fundamental to ensure the correct execution of safety-critical processes. In this paper, we introduce a general architecture that integrates Large Language Models (LLMs) with automated planning, enabling humans to specify high-level activities (also referred to as processes) using natural language, and to monitor their execution by querying a robot. We also present an implementation of this architecture using state-of-the-art components and quantitatively evaluate the approach in a real-world precision agriculture scenario.

This paper proposes a formal account of AI trustworthiness, connecting both intrinsic and perceived trustworthiness in an operational schematization. We argue that trustworthiness extends beyond the inherent capabilities of an AI system to include significant influences from observers' perceptions, such as perceived transparency, agency locus, and human oversight. While the concept of perceived trustworthiness is discussed in the literature, few attempts have been made to connect it with the intrinsic trustworthiness of AI systems. Our analysis introduces a novel schematization to quantify trustworthiness by assessing the discrepancies between expected and observed behaviors and how these affect perceived uncertainty and trust. The paper provides a formalization for measuring trustworthiness, taking into account both perceived and intrinsic characteristics. By detailing the factors that influence trust, this study aims to foster more ethical and widely accepted AI technologies, ensuring they meet both functional and ethical criteria.

Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks. However, LLMs and VLMs can produce erroneous results, especially when a deep understanding of the problem domain is required. For instance, when planning and perception are needed simultaneously, these models often struggle because of difficulties in merging multi-modal information. To address this issue, fine-tuned models are typically employed and trained on specialized data structures representing the environment. This approach has limited effectiveness, as it can overly complicate the context for processing. In this paper, we propose a multi-agent architecture for embodied task planning that operates without the need for specific data structures as input. Instead, it uses a single image of the environment, handling free-form domains by leveraging commonsense knowledge. We also introduce a novel, fully automatic evaluation procedure, PG2S, designed to better assess the quality of a plan. We validated our approach using the widely recognized ALFRED dataset, comparing PG2S to the existing KAS metric to further evaluate the quality of the generated plans.

The problem of multi-object tracking (MOT) consists in detecting and tracking all the objects in a video sequence while keeping a unique identifier for each object. It is a challenging and fundamental problem for robotics. In precision agriculture the challenge of achieving a satisfactory solution is amplified by extreme camera motion, sudden illumination changes, and strong occlusions. Most modern trackers rely on the appearance of objects rather than motion for association, which can be ineffective when most targets are static objects with the same appearance, as in the agricultural case. To this end, on the trail of SORT [5], we propose AgriSORT, a simple, online, real-time tracking-by-detection pipeline for precision agriculture based only on motion information that allows for accurate and fast propagation of tracks between frames. The main focuses of AgriSORT are efficiency, flexibility, minimal dependencies, and ease of deployment on robotic platforms. We test the proposed pipeline on a novel MOT benchmark specifically tailored for the agricultural context, based on video sequences taken in a table grape vineyard, particularly challenging due to strong self-similarity and density of the instances. Both the code and the dataset are available for future comparisons at: https://github.com/Lio320/AgriSORT

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited availability of labeled data, class imbalance and the inherent variability and complexity of satellite images. In order to mitigate those issues, our study explores the effectiveness of a Cut-and-Paste augmentation technique for semantic segmentation in satellite images. We adapt this augmentation, which usually requires labeled instances, to the case of semantic segmentation. By leveraging the connected components in the semantic segmentation labels, we extract instances that are then randomly pasted during training. Using the DynamicEarthNet dataset and a U-Net model for evaluation, we found that this augmentation significantly enhances the mIoU score on the test set from 37.9 to 44.1. This finding highlights the potential of the Cut-and-Paste augmentation to improve the generalization capabilities of semantic segmentation models in satellite imagery.

Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the challenge of maintaining low computational overhead given the limited resources of robotic hardware. We introduce EMPOWER, a framework designed for open-vocabulary online grounding and planning for embodied agents aimed at addressing these issues. By leveraging efficient pre-trained foundation models and a multi-role mechanism, EMPOWER demonstrates notable improvements in grounded planning and execution. Quantitative results highlight the effectiveness of our approach, achieving an average success rate of 0.73 across six different real-life scenarios using a TIAGo robot.

Virtual Reality (VR) environments are used as a way to let humans experiment with algorithms, techniques, and situations in various application areas, including emergency management, serious games, smart manufacturing, and precision agriculture. They are especially relevant when experiments in the real world may be harmful to human operators. As VR environments are the closest possible faithful replicas of real environments, many recent works focus on the employment of such tools as a means to generate synthetic datasets that can be used for training machine and deep learning models, especially in situations where obtaining real datasets can be difficult. In this paper, we introduce a strategy to generate a dataset for pose estimation in the challenging scenario of precision agriculture. Finally, the quality of the generated dataset was evaluated.

Human-Robot Interaction (HRI) has become increasingly important as robots have been integrated into various daily life aspects. Gesture recognition plays a crucial role in non-verbal communication in HRI. Indeed, the real-time robot's interpretation of human gestures is essential for enhancing the overall user experience. To this aim, improving the accuracy and efficiency of gesture recognition in HRI could be achieved by integrating Virtual Reality (VR) technology. In this article, we describe the development of virtually-generated avatars and a gesture recognition system that uses machine learning techniques to allow ground robots to recognize gestures performed by both digital agents and actual humans. Nevertheless, we address some existing challenges by presenting a set of gesture definitions, data generation, and system evaluation using virtual reality simulation. Our findings and results highlight the importance of addressing these challenges to enable effective gesture recognition in HRI. In particular, we demonstrate that the adoption of VR can significantly increase the system's accuracy and efficiency, improving the overall user experience.

Collaborative robots seamlessly share the space with humans in production scenarios such as those involved in smart manufacturing and agriculture, thus raising several human safety concerns. Since a collaboration between humans and robots is performed through communicative acts, applying accurate techniques for understanding them is of the utmost importance to guarantee the overall safety of the human. A preliminary classification of the communicative acts into categories is required to increase the accuracy of adopted methods and the promptness of the response. This paper evaluates a speech communicative act classification methodology in the challenging scenario of precision agriculture using Virtual Reality (VR). Our proposal can easily be applied to any production scenario involving collaborative robots.

Abstract Deep neural networks are the driving force of the recent explosion of machine learning applications in everyday life. However, they usually require a lot of training data to work well, and they act as black-boxes, making predictions without any explanation about them. This paper presents Memory Wrap, a module (i.e, a set of layers) that can be added to deep learning models to improve their performance and interpretability in settings where few data are available. Memory Wrap adopts a sparse content-attention mechanism between the input and some memories of past training samples. We show that adding Memory Wrap to standard deep neural networks improves their performance when they learn from a limited set of data, and allows them to reach comparable performance when they learn from the full dataset. We discuss how the analysis of its structure and content-attention weights helps to get insights about its decision process and makes their predictions more interpretable, compared to the same networks without Memory Wrap. We test our approach on image classification tasks using several networks on three different datasets, namely CIFAR10, SVHN, and CINIC10.

Deep reinforcement learning has achieved impressive results in recent years; yet, it is still severely troubled by environments showcasing sparse rewards. On top of that, not all sparse-reward environments are created equal, i.e., they can differ in the presence or absence of various features, with many of them having a great impact on learning. In light of this, the present work puts together a literature compilation of such environmental features, covering particularly those that have been taken advantage of and those that continue to pose a challenge. We expect this effort to provide guidance to researchers for assessing the generality of their new proposals and to call their attention to issues that remain unresolved when dealing with sparse rewards.

For performing robotic manipulation tasks, the core problem is determining suitable trajectories that fulfill the task requirements. Various approaches to compute such trajectories exist, being learning and optimization the main driving techniques. Our work builds on the learning-from-demonstration (LfD) paradigm, where an expert demonstrates motions, and the robot learns to imitate them. However, expert demonstrations are not sufficient to capture all sorts of task specifications, such as the timing to grasp an object. In this paper, we propose a new method that considers formal task specifications within LfD skills. Precisely, we leverage Signal Temporal Logic (STL), an expressive form of temporal properties of systems, to formulate task specifications and use black-box optimization (BBO) to adapt an LfD skill accordingly. We demonstrate our approach in simulation and on a real industrial setting using several tasks that showcase how our approach addresses the LfD limitations using STL and BBO.

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition1. Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well.

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of Human-Robot Interaction (HRI) teaming tasks to prove functionality and promote adoption. In this article, we present a general-purpose and repeatable methodology for conducting studies in collaborative HRI in the range of robotic competitions. The methodology includes a step-by-step approach to design HRI teaming tasks tailored to be enacted in a robotic competition and to evaluate the performance of social robots to execute the designed tasks, exploring the relationship between robots’ performance and user perceptions based on the feedback of the users participating to such tasks. We assess the feasibility of the methodology to design and evaluate an HRI teaming task in the context of “Smart CIties RObotics Challenges” (SciRoc) competition, which targets at investigating the impact of social of robots in smart cities.

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitionsRoboCuphas a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within theRoboCupframework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games.

In this paper, we present the outcomes of the first user study designed and evaluated in the context of the Smart City Robotics Challenge (SciRoc Challenge). The study presented in this paper has the main novelty of having been devised and implemented in a realistic environment: a robot competition where robot tasks were developed by participant teams, robots were fully autonomous, and user questionnaires were part of the competition score. Specifically, our study was performed over a scenario configured to instruct a robot to take an elevator of a shopping mall asking for customers support. Leveraging the dedicated questionnaire designed for the tested scenario, we validated the experimental hypothesis if user perception of robots' behaviour may be influenced by the user's gender. In the end, we discuss the results of our study.

In this paper we present a novel mechanism to get explanations that allow to better understand network predictions when dealing with sequential data. Specifically, we adopt memory-based networks — Differential Neural Computers — to exploit their capability of storing data in memory and reusing it for inference. By tracking both the memory access at prediction time, and the information stored by the network at each step of the input sequence, we can retrieve the most relevant input steps associated to each prediction. We validate our approach (1) on a modified T-maze, which is a non-Markovian discrete control task evaluating an algorithm’s ability to correlate events far apart in history, and (2) on the Story Cloze Test, which is a commonsense reasoning framework for evaluating story understanding that requires a system to choose the correct ending to a four-sentence story. Our results show that we are able to explain agent’s decisions in (1) and to reconstruct the most relevant sentences used by the network to select the story ending in (2). Additionally, we show not only that by removing those sentences the network prediction changes, but also that the same are sufficient to reproduce the inference.

Weed spotting through image classification is one of the methods applied in precision agriculture to increase efficiency in crop damage reduction. These classifications are nowadays typically based on deep machine learning with convolutional neural networks (CNN), where a main difficulty is gathering large amounts of labeled data required for the training of these networks. Thus, synthetic dataset sources have been developed including simulations based on graphic engines; however, some data inputs that can improve the performance of CNNs like the near infrared (NIR) have not been considered in these simulations. This paper presents a simulation in the Unity game engine that builds fields of sugar beets with weeds. Images are generated to create datasets that are ready to train CNNs for semantic segmentation. The dataset is tested by comparing classification results from the bonnet CNN network trained with synthetic images and trained with real images, both with RGB and RGBN (RGB+near infrared) as inputs. The preliminary results suggest that the addition of the NIR channel to the simulation for plant-weed segmentation can be effectively exploited. These show a difference of 5.75% for the global mean IoU over 820 classified images by including the NIR data in the unity generated dataset.

In this paper, we present the results of a recent user study that investigates if user perception of HRI in social contexts may be affected by changing the interaction modality with the robot. Leveraging on Robot Social Attribute Scale (RoSAS) survey and on a statistical analysis, our results show that, in some interaction modalities, a greater feeling of discomfort is felt by users interacting with the robot. Interestingly, results also show the influence of users' gender on the user perception.

In this paper, we present an approach to develop a new type of questionnaire for evaluating customers' perceptions in the upcoming Smart CIty RObotic Challenge (SciRoc). The approach consists of two steps. First, it relies on interviewing experts on Human-Robot Interaction (HRI) to understand which robot's behaviours can potentially affect the users' perceptions during a HRI task. Then, it leverages a user survey to filter out those robot's behaviours that are not significantly relevant from the end user perspective. We concretely enacted our approach over a specific scenario developed in the context of SciRoc, which instructs a robot to take an elevator of a shopping mall asking support to the customers of the mall. The results of the survey have allowed us to derive a final list of 17 behaviours to be captured in the questionnaire, which has been finally developed relying on a 5-point Likert-scale.

[No abstract available]

The advent of the so-called Companion Robots is raising many ethical concerns among scholars and in the public opinion. Focusing mainly on robots caring for the elderly, in this paper we analyze these concerns to distinguish which are directly ascribable to robotic, and which are instead pre-existent. One of these is the "deception objection", namely the ethical unacceptability of deceiving the user about the simulated nature of the robot's behaviors. We argue on the inconsistency of this charge, as today formulated. After that, we underline the risk, for human-robot interaction, to become a hallucinatory relation where the human would subjectify the robot in a dynamic of meaning-overload. Finally, we analyze the definition of "quasi-other" relating to the notion of "uncanny". The goal of this paper is to argue that the main concern about Companion Robots is the simulation of a human-like interaction in the absence of an autonomous robotic horizon of meaning. In addition, that absence could lead the human to build a hallucinatory reality based on the relation with the robot.

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g, hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q- CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols.

Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing vessel traffic services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, which are collected from multiple real sites, with different light, weather, and traffic conditions.

In this paper, we address coordination within a team of cooperative autonomous robots that need to accomplish a common goal. Our survey of the vast literature on the subject highlights two directions to further improve the performance of a multi-robot team. In particular, in a dynamic environment, coordination needs to be adapted to the different situations at hand (for example, when there is a dramatic loss of performance due to unreliable communication network). To this end, we contribute a novel approach for coordinating robots. Such an approach allows a robotic team to exploit environmental knowledge to adapt to various circumstances encountered, enhancing its overall performance. This result is achieved by dynamically adapting the underlying task assignment and distributed world representation, based on the current state of the environment. We demonstrate the effectiveness of our coordination system by applying it to the problem of locating a moving, non-adversarial target. In particular, we report on experiments carried out with a team of humanoid robots in a soccer scenario and a team of mobile bases in an office environment.

The detection of malignant lesions in dermoscopic images by using automatic diagnostic tools can help in reducing mortality from melanoma. In this paper, we describe a fully-automatic algorithm for skin lesion segmentation in dermoscopic images. The proposed approach is highly accurate when dealing with benign lesions, while the detection accuracy significantly decreases when melanoma images are segmented. This particular behavior lead us to consider geometrical and color features extracted from the output of our algorithm for classifying melanoma images, achieving promising results.

Modern robotic architectures are equipped with sensors enabling a deep analysis of the environment. In this work, we aim at demonstrating that such perceptual information (here modeled through semantic maps) can be effectively used to enhance the language understanding capabilities of the robot. A robust lexical mapping function based on the Distributional Semantics paradigm is here proposed as a basic model of grounding language towards the environment. We show that making such information available to the underlying language understanding algorithms improves the accuracy throughout the entire interpretation process.

As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.

In this paper, we consider several autonomous robots with separate tasks that require coordination, but not a coupling at every decision step. We assume that each robot separately acquires its task, possibly from different providers. We address the problem of multiple robots incrementally acquiring tasks that require their sparse-coordination. To this end, we present an approach to provide tasks to multiple robots, represented as sequences, conditionals, and loops of sensing and actuation primitives. Our approach leverages principles from sparse-coordination to acquire and represent these joint-robot plans compactly. Specifically, each primitive has associated preconditions and effects, and robots can condition on the state of one another. Robots share their state externally using a common domain language. The complete sparse-coordination framework runs on several robots. We report on experiments carried out with a Baxter manipulator and a CoBot mobile service robot.

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 understand the robot internal representation through dialog. To this end, we introduce the concept of sensing descriptors . Such representations are used by the robot to recognize unknown object properties in the given commands and warn the user about them. Additionally, we show how these properties can be learned over time by leveraging past interactions in order to enhance the grounding capabilities of the robot.

The key issue investigated in the field of Multi-Robot Systems (MRS) is the problem of coordinating multiple robots in a common environment. In tackling this issue, problems concerning the capabilities of multiple heterogeneous robots and their environmental constraints need to be faced. In this paper, we introduce a novel approach for coordinating a team of robots. The key contribution of the proposed method consists in exploiting the rules governing the scenario by identifying and using “contexts”. The robots actions and perceptions are specialized to the current context to enhance both single and collective behaviors. The presented approach has been largely validated in a RoboCup scenario. In particular, we adopt a soccer environment as a testing ground for our algorithm. We evaluate our method in several testing sessions on a simulator representing a virtual model of a soccer field. The obtained results show a substantial improvement of the team adopting our algorithm.

Robots are slowly becoming part of everyday life, as they are being marketed for commercial applications (viz. telepresence, cleaning or entertainment). Thus, the ability to interact with non-expert users is becoming a key requirement. Even if user utterances can be efficiently recognized and transcribed by Automatic Speech Recognition systems, several issues arise in translating them into suitable robotic actions. In this paper, we will discuss both approaches providing two existing Natural Language Understanding workflows for Human Robot Interaction. First, we discuss a grammar based approach: it is based on grammars thus recognizing a restricted set of commands. Then, a data driven approach, based on a free-from speech recognizer and a statistical semantic parser, is discussed. The main advantages of both approaches are discussed, also from an engineering perspective, i.e. considering the effort of realizing HRI systems, as well as their reusability and robustness. An empirical evaluation of the proposed approaches is carried out on several datasets, in order to understand performances and identify possible improvements towards the design of NLP components in HRI.

This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e.g., grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e.g., to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.

In large-scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high-level, net-centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical to achieve a prompt reaction as needed in the operational scenario. In this paper, we propose a multiagent-based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multiagent situation assessment, which allows the agents to achieve coherent high-level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required. © 2012 Wiley Periodicals, Inc.

In this paper we present some results obtained through an experimental evaluation of tangible user interfaces (TUIs), comparing their novel interaction paradigms with more conventional interfaces, such as a joypad and a keyboard. Our main goal is to make a formal assessment of TUIs in robotics through a rigorous and extensive experimental evaluation. Firstly, we identified the main benefits of TUIs for robot teleoperation in a urban search and rescue task. Secondly, we provide an evaluation framework to allow for an effective comparison of tangible interfaces with other input devices.

Multi-robot patrolling is a fundamental functionality for multi-robot surveillance and environmental monitoring and has been longly investigated. However, benchmarks for multi-robot patrolling, the realization of realistic simulators and testing on real robotic platforms are still very limited. In this paper we discuss the application of state-of-the-art patrolling strategies in realistic applications, showing that it is important to take into account: perception needs of the robots, uncertainty on action execution, characteristics of the environment, closed-loop coordinated behaviors, and realistic simulation environments.

Tangible user interfaces (TUIs) exhibit innovative interaction paradigms, for example through motion sensing, tactile feedback, or gesturing. Whilst wide spread in human-computer interaction, their value in robotic systems has still to be assessed. In this paper, we present some results obtained through an extensive experimental evaluation of motion sensing interaction paradigms implemented on TUIs for the teleoperation of ground robots. Once analyzed the collected data, in order to evince relevant properties of TUIs, we provide a detailed discussion of our results in terms of mission-related performance, environment conditions, robot operation degree, and human cognitive effort. Our belief is that such a study represents a valuable step towards a formal assessment of tangible interfaces in robotics.

The execution of tasks for a robotic agent embedded in a dynamic environment brings about several challenges, due to unpredictable (or unobservable) events, and to inaccurate perception. Moreover, the agent can perform multiple tasks and each task can be achieved by applying different plans, therefore the decision about which strategy is the most convenient, given the current situation of the world, is important for assessing an intelligent overall behavior of the agent. This paper tackles the problem of on-line execution monitoring in a novel way with respect to previous work, since: (1) it considers uncertainty in the duration of actions with a probabilistic model of action duration; (2) it evaluates the cost of each possible plan at run-time in terms of probability of successful termination within a desired expected time. The approach has been evaluated both in a robotic soccer and a surveillance scenario.

We focus on the aspect of sensing in reasoning about actions under qualitative and probabilistic uncertainty. We first define the action language E for reasoning about actions with sensing, which has a semantics based on the autoepistemic description logic ALCK NF , and which is given a formal semantics via a system of deterministic transitions between epistemic states. As an important feature, the main computational tasks in E can be done in linear and quadratic time. We then introduce the action language E + for reasoning about actions with sensing under qualitative and probabilistic uncertainty, which is an extension of E by actions with nondeterministic and probabilistic effects, and which is given a formal semantics in a system of deterministic, nondeterministic, and probabilistic transitions between epistemic states. We also define the notion of a belief graph, which represents the belief state of an agent after a sequence of deterministic, nondeterministic, and probabilistic actions, and which compactly represents a set of unnormalized probability distributions. Using belief graphs, we then introduce the notion of a conditional plan and its goodness for reasoning about actions under qualitative and probabilistic uncertainty. We formulate the problems of optimal and threshold conditional planning under qualitative and probabilistic uncertainty, and show that they are both uncomputable in general. We then give two algorithms for conditional planning in our framework. The first one is always sound, and it is also complete for the special case in which the relevant transitions between epistemic states are cycle-free. The second algorithm is a sound and complete solution to the problem of finite-horizon conditional planning in our framework. Under suitable assumptions, it computes every optimal finite-horizon conditional plan in polynomial time. We also describe an application of our formalism in a robotic-soccer scenario, which underlines its usefulness in realistic applications.

The ICP algorithm has been extensively used in computer vision for regis-tration and tracking purposes. The original formulation of this method is restricted to the use of non-articulated models. A straightforward generali-sation to articulated structures is achievable through the joint minimisation of all the structure pose parameters, for example using Levenberg-Marquardt (LM) optimisation. However, in this approach the aligning transformation cannot be estimated in closed form, like in the original ICP, and the approach heavily suffers from local minima. To overcome this limitation, some au-thors have extended the straightforward generalisation at the cost of giving up some of the properties of ICP. In this paper, we present a generalisation of ICP to articulated structures, which preserves all the properties of the original algorithm. The key idea is to divide the articulated body into parts, which can be aligned rigidly in the way of the original ICP, with additional constraints to keep the articulated structure intact. Experiments show that our method reduces the residual registration error by a factor of ≈2. 1

Intense efforts to define a common structure in robotic applications, both from a conceptual and from an implementation point of view, have been carried out in the last years and several frameworks have been realized for helping in developing robotic applications. However, due to the diversity of these applications, as well as of the research groups involved, a common framework is still far from being accepted. In this paper we focus on modularity and re-usability, as major features for robotic applications. We thus characterize existing frameworks for robot software development through the choices made on concurrent execution of modules and information sharing among them and we present OpenRDK, a modular framework focused on rapid development of distributed robotic systems. OpenRDK has been designed and developed with many years of experience following userspsila advice and has been successfully used for the development of many diverse applications with different kinds of robots. After such an extensive test, OpenRDK is now an open source project.

Human robot interaction factors for controlling remote mobile robots is a recent field of research. In this paper we propose a novel system that dynamically adjusts the interaction between operators and robots according to the mission requirements. In particular we focus on the task allocation service. This service allocates the control of robots deployed in a mission between the artificial agents (autonomy) and the operators (tele-operation), being capable of detecting problems derived from autonomy, diagnose the causes, and alert the operator. The system is designed for a multi-robot-multi-user paradigm.

In this paper we present a novel technique to estimate the state of heterogeneous features from inaccurate sensors. The proposed approach exploits the reliability of the feature extraction process in the sensor model and uses a Rao-Blackwellized particle filter to address the data association problem. Experimental results show that the use of reliability improves performance by allowing the approach to perform better data association among detected features. Moreover, the method has been tested on a real robot during an exploration task in a non-planar environment. This last experiment shows an improvement in correctly detecting and classifying interesting features for navigation purpose.

To coordinate a team of robots for exploration is a challenging problem, particularly in large areas as for example the devastated area after a disaster. This problem can generally be decomposed into task assignment and multi-robot path planning. In this paper, we address both problems jointly. This is possible because we reduce significantly the size of the search space by utilizing RFID tags as coordination points. The exploration approach consists of two parts: a stand-alone distributed local search and a global monitoring process which can be used to restart the local search in more convenient locations. Our results show that the local exploration works for large robot teams, particularly if there are limited computational resources. Experiments with the global approach showed that the number of conflicts can be reduced, and that the global coordination mechanism increases significantly the explored area.

Recently, Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao-Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to re-use an already computed proposal distribution. Both techniques substantially speed up the overall process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published, standard datasets illustrate the advantages of our methods over previous Rao-Blackwellized mapping approaches

The problem of assigning tasks to a group of robots acting in a dynamic environment is a fundamental issue for a multirobot system (MRS) and several techniques have been studied to address this problem. Such techniques usually rely on the assumption that tasks to be assigned are inserted into the system in a coherent fashion. In this work we consider a scenario where tasks to be accomplished are perceived by the robots during mission execution. This issue has a significative impact on the task allocation process and, at the same time, makes it strictly dependent on perception capabilities of robots. More specifically, we present an asynchronous distributed mechanism based on Token Passing for allocating tasks in a team of robots. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots; our scenario comprises a set of robots that must cooperatively collect a set of objects scattered in the working environment. Each object collection task requires the cooperation of two robots. The experiments in the simulation environment allowed us to extract quantitative data from several missions and in different operative conditions and to characterize in a statistical way the results of our approach, especially when the team size increases

Autonomous surveillance and monitoring of structures is one of the most studied tasks in the past years. In this paper we present an approach in mobile robotic surveillance to detect the presence of people or other moving objects in scenario and classify them as human body and not human body by stereo vision.

In this paper we present an asynchronous distributed mechanism for allocating tasks in a team of robots. Tasks to be allocated are dynamically perceived from the environment and can be tied by execution constraints. Conflicts among team mates arise when an uncontrolled number of robots execute the same task, resulting in waste of effort and spatial conflicts. The critical aspect of task allocation in Multi Robot Systems is related to conflicts generated by limited and noisy perception capabilities of real robots. This requires significant extensions to the task allocation techniques developed for software agents. The proposed approach is able to successfully allocate roles to robots avoiding conflicts among team mates and maintaining low communication overhead. We implemented our method on AIBO robots and performed quantitative analysis in a simulated environment.

Multirobot systems (MRS) are, nowadays, an important research area within robotics and artificial intelligence and a growing number of systems have recently been presented in the literature. Since application domains and tasks that are faced by MRS are of increasing complexity, the ability of the robots to cooperate can be regarded as a fundamental feature. In this paper, we present a survey of the recent work in the area by specifically examining the forms of cooperation and coordination realized in the MRS. In particular, we propose a new taxonomy for classification of the approaches to coordination in MRS and we describe some systems, which we consider representative in our taxonomy. We finally discuss the outcomes of our analysis and try to highlight future trends of the research on MRS.

The activities of search and rescue of victims in large-scale disasters are very relevant social problems, and from a scientific viewpoint raise many different technical problems in the fields of artificial intelligence, robotics and multi agent systems. In this paper we describe the development of a multi agent system based on the RoboCup Rescue simulator to allow monitoring and decision support, that are needed in a rescue operation. Two significant accomplishments are reported in this paper: the first is a framework for cognitive agent development that provides for the capabilities of information fusion, planning and coordination; the second one is a methodology for evaluation of multi-agent systems in this scenario that aims at measuring not only the efficiency of a system, but also its robustness when conditions in the environment change.

Multi-Robot Systems (MRS) are, nowadays, an important research area within Robotics and Artificial Intelligence and a growing number of systems have been recently presented in the literature. In this paper we present an analysis of the most relevant works on MRS by specifically examining their cooperative aspects. In particular, we propose a new taxonomy for a fine and precise analysis of the recent works on MRS and we describe some approaches which we consider representative in our taxonomy. We finally discuss the outcome of our analysis and try to highlight future trends of the research on MRS.

Knowing the position of a mobile robot in the environment in which it operates is an important element for effectively accomplishing complex tasks requiring autonomous navigation. Among several existing techniques for robot self-localization, a new approach called Hough localization was proposed for map matching in the Hough domain, that turned out to be reliable and efficient for position tracking in polygonal environments. In this paper we present an extension of Hough localization which is able to deal with the global localization problem, in which the robot does not know its initial position in the environment.

We describe the design and implementation of a biped locomotion strategy for the robot Sony ERS-210 (AIBO). Being designed for quadruped gaits, this robot has several limitations which make biped locomotion a challenging task, such as passive feet, a high barycenter in the erect posture, and relatively weak actuators. We have therefore chosen to fully exploit the double support phase, in which the robot has both feet on the ground, in order to achieve the correct take-off conditions for performing the single support phase. During the latter, the mechanism motion is essentially uncontrolled but can be predicted and planned using a simple equivalent mechanical system. Both simulation and experimental results show the positive outcome of our study.

We present description logics of minimal knowledge and negation as failure (MKNF-DLs), which augment description logics with modal operators interpreted according to Lifschitz's nonmonotonic logic MKNF. We show the usefulness of MKNF-DLs for a formal characterization of a wide variety of nonmonotonic features that are both commonly available inframe-based systems, and needed in the development of practical knowledge-based applications: defaults, integrity constraints, role, and concept closure. In addition, we provide a correct and terminating calculus for query answering in a very expressive MKNF-DL.

Autonomous navigation for mobile robots performing complex tasks over long periods of time requires effec-tive and robust self-localization techniques. In this paper we describe a probabilistic approach to self-localization that integrates Kalman filtering with map matching based on the Hough Transform. Several sys-tematic experiments for evaluating the approach have been performed both on a simulator and on soccer robots embedded in the RoboCup environment. 1

Information integration is one of the most important aspects of a Data Warehouse. When data passes from the sources of the application-oriented operational environment to the Data Warehouse, possible inconsistencies and redundancies should be resolved, so that the warehouse is able to provide an integrated and reconciled view of data of the organization. We describe a novel approach to data integration in Data Warehousing. Our approach is based on a conceptual representation of the Data Warehouse application domain, and follows the so-called local-as-view paradigm: both source and Data Warehouse relations are defined as views over the conceptual model. We propose a technique for declaratively specifying suitable reconciliation correspondences to be used in order to solve conflicts among data in different sources. The main goal of the method is to support the design of mediators that materialize the data in the Data Warehouse relations. Starting from the specification of one such relation as a query over the conceptual model, a rewriting algorithm reformulates the query in terms of both the source relations and the reconciliation correspondences, thus obtaining a correct specification of how to load the data in the materialized view.

Coordination among multiple robots has been extensively studied, since a number of practical tasks can be performed in a more effective way by employing a fleet of coordinated robotic bases. In particular, distributed coordination among robotic agents has been considered within the framework offered by the robotic soccer competitions. We describe the methods and the results achieved in coordinating the players of the ART team participating in the RoboCup F-2000 league. The team is formed by several heterogeneous robots having different mechanics, different sensors, different control software, and, in general, different abilities for playing soccer. The coordination framework we have developed has been successfully applied during the 1999 official competitions allowing both for a significant improvement of the overall team performance and for a complete interchangeability of all the robots.

We discuss the main methodological and technological issues arosen in the last years in the development of the enterprise integrated database of Telecom Italia and, subsequently in the management of the primary data store for Telecom Italia data warehouse applications.

The notion of class is ubiquitous in computer science and is central in many formalisms for the representation of structured knowledge used both in knowledge representation and in databases. In this paper we study the basic issues underlying such representation formalisms and single out both their common characteristics and their distinguishing features. Such investigation leads us to propose a unifying framework in which we are able to capture the fundamental aspects of several representation languages used in different contexts. The proposed formalism is expressed in the style of description logics, which have been introduced in knowledge representation as a means to provide a semantically well-founded basis for the structural aspects of knowledge representation systems. The description logic considered in this paper is a subset of first order logic with nice computational characteristics. It is quite expressive and features a novel combination of constructs that has not been studied before. The distinguishing constructs are number restrictions, which generalize existence and functional dependencies, inverse roles, which allow one to refer to the inverse of a relationship, and possibly cyclic assertions, which are necessary for capturing real world domains. We are able to show that it is precisely such combination of constructs that makes our logic powerful enough to model the essential set of features for defining class structures that are common to frame systems, object-oriented database languages, and semantic data models. As a consequence of the established correspondences, several significant extensions of each of the above formalisms become available. The high expressiveness of the logic we propose and the need for capturing the reasoning in different contexts forces us to distinguish between unrestricted and finite model reasoning. A notable feature of our proposal is that reasoning in both cases is decidable. We argue that, by virtue of the high expressive power and of the associated reasoning capabilities on both unrestricted and finite models, our logic provides a common core for class-based representation formalisms.

We describe an approach to reasoning agents which is based on a formal theory of actions and is actually implemented on a mobile robot working in an office environment. From an epistemiological viewpoint, our proposal is originated by the correspondence between Dynamic Logics and Description Logics, Specifically, we consider an epistemic extension of Description Logics to provide a new theoretical framework for the representation of dynamic systems, where the agent's reasoning is based on its knowledge about the world. In this setting, we obtain a weaker notion of logical inference, thus simplifying the reasoning task. From a practical viewpoint, we use a general purpose knowledge representation system based on Description Logics and its associated reasoning tools, in order to plan the actions of the mobile robot 'Tino', starting from the knowledge about the environment and the action specification. In addition, we exploit the robot's capabilities in order to integrate the execution of the plan with reactive behaviours, thus enabling the agent to accomplish its tasks in the real world.

Information integration is one of the core problems in cooperative information systems. The authors argue that two critical factors for the design and maintenance of applications requiring information integration are conceptual modeling of the domain, and reasoning support over the conceptual representation. In particular they present a general architecture for information integration that explicitly includes a conceptual representation of the application. They illustrate how the architecture can express several integration settings and existing systems. They provide various arguments in favor of the conceptual level in the architecture and of automated reasoning over the conceptual representation. Finally, they present a specific proposal of an integration system which realizes the general architecture and is equipped with decidable reasoning procedures.

In this paper we present a novel approach to accessing the Web, that enables automatically acquiring data from Web sites and making them accessible to the user through a database query paradigm. The basic idea is to build, once the user has specified a generic domain of interest, the domain conceptual representation, to instantiate it with data extracted from Web sites (so to build a materialized view over the Web), and to query such a conceptual representation through an easy-to-use visual interface. Knowledge representation techniques are used for both the internal modeling of the conceptual representation and for supporting the automatic extraction of data from Web sites to feed the materialized view. We describe a prototype implementation, focusing on the internal representation of information and on the process for analysing Web sites and acquiring data from them. Our preliminary results support our intuition that, at least for certain kinds of queries, the proposed approach can effectively provide the desired information.

The Internet revolution has made an enormous quantity of information available to a disparate variety of people. The amount of information, the typical access modality (that is, browsing), and the rapid growth of the Net, force the user, while searching for the information of interest, to dip into multiple sources, in a labyrinth of millions of links. Web-at-A-Glance (WAG) is a system allowing the user to query (instead of browsing) the Web. WAG performs this ambitious task by constructing a personalized database, pertinent to the user's interests. The system semi-automatically gleans the most relevant information from a Web site or several Web sites, stores them into a database cooperatively designed with the user, and allows her/him to query such a database through a visual interface equipped with a powerful multimedia query language. This paper presents the design philosophy, the architecture and the core of the WAG system. A prototype WAG is being implemented to test the feasibility of the proposed approach.

In this paper we address ground logics, a family of nonmonotonic modal logics, and their usage in knowledge representation. In such a setting non-modal sentences are used to represent the knowledge of an agent about the world, while an epistemic operator provides the agent with autoepistemic or introspective knowledge. Ground logics are based on the idea of characterizing the knowledge of the agent by allowing it to make nonmonotonic assumptions only with respect to the knowledge about the world, i.e. expressed by nonmodal formulae. They are characterized by a fix-point equation which determines the set of formulae derivable from the agent's initial knowledge and which can be applied to different normal modal systems to obtain a variety of nonmonotonic modal logics. In the paper we address the semantical, computational and epistemological properties of ground logics. We provide a semantic characterization of ground logics by defining a preference relation on possible-world models based on the minimization of the knowledge expressed by nonmodal formulae. We analyse the computational complexity of reasoning in ground logics, providing both a lower bound through a reduction from quantified Boolean formulae and an upper bound through an algorithm for computing logical entailment. We discuss the representational features of ground logics, in particular defaults, and provide a thorough comparison with McDermott and Doyle's logics.

It is a common opinion that subsumption is the central reasoning task in frame-based knowledge representation languages (or concept languages). Intuitively, a concept C subsumes another concept D if the set of objects represented by C is a superset of the one represented by D. When individual objects are taken into account, the basic deductive task for retrieving information from a knowledge base is instance checking, that amounts to checking whether the knowledge base implies that an individual is an instance of a given concept. In this paper, we address the question of whether instance checking can be solved by means of subsumption algorithms. We do so by considering several languages where subsumption belongs to different complexity classes. For such languages we present methods for the instance checking problem, provide a complexity analysis of this problem, and compare it with the subsumption problem. The main result of the paper is that instance checking is not always easily reducible to subsumption. In particular, there are cases where it is strictly harder than subsumption. This impacts on the design of reasoning algorithms for knowledge representation systems based on concept languages.