The introduction of social robots in preschool settings has become a common research strategy for addressing educational challenges. Although teachers and educators play a central role in classroom dynamics, they are often underrepresented in studies on educational robots. Often, robots are presented as “black-boxes”, with little attention paid to providing teachers with dedicated training. This study describes the design and implementation of the Teacher Professional Development Research (TPDR) as a structured method for integrating social robots into early education, supporting teachers and educators. TPDR is an established educational practice that addresses pedagogical issues by engaging teachers as actors in the research process. Our project involved deploying a robot in four preschools and one nursery with a multicultural setting, primarily to foster intercultural integration. Both quantitative and qualitative data were collected to evaluate the impact of this approach on teachers' attitudes and willingness to adopt the robot. Findings indicate that the teachers gained a greater awareness of the robot’s social presence and a clearer understanding of its educational potential. There was also an overall positive shift in their intercultural sensitivity.

The objective of this work is to develop and evaluate computational cognitive models of Theory of Mind (ToM) and Machiavellian behavior embedded in a humanoid robot. Machiavellianism, together with psychopathy and narcissism, is part of the Dark Triad (DT), three constructs that correspond to socially aversive yet not necessarily pathological personalities. The motivations of the present work are both theoretical and application-oriented. In the long term, we aim to: (i) Provide researchers with new insights into the Machiavellian as well as other DT constructs through simulated and robotic setups; (ii) Provide a tool to train psychologists to deal with social and antisocial behavior in a controlled setup; (iii) Help people become aware of the behavioral mechanisms that they may expect from people with DT traits in social and affective relationships; (iv) Assist robotic engineers in developing better robots by identifying behaviors that should be avoided. To this end, we explored a computational model of ToM in the popular Planning Domain Definition Language (PDDL), and defined a domain with the necessary elements to induce Machiavellian behavior during planning and execution. Subsequently, we implemented our computational model in a software architecture controlling the behavior of a humanoid robot and recorded videos of the robot interacting with two actors. Finally, we conducted experiments with 300 participants divided into 6 conditions to verify whether the implemented framework is versatile enough to generate behaviors that participants would rate as either more Machiavellian or less Machiavellian based on their observations of the recorded videos.

Co-design is widely used in educational contexts to involve stakeholders and make them active participants in the learning process. This study presents the co-design process conducted with teachers, educators, and families before introducing a social robot in four Italian preschools and a nursery. The robot is expected to promote intercultural awareness in a highly culturally diverse educational environment. We consider the co-design process an essential step, as teachers and educators, by knowing the social rules and pedagogical concepts of each specific educational context, can tailor the unique characteristics of the robot (being embodied and equipped with social behaviors) to effectively benefit their pedagogical reality. In addition, stakeholders, through co-design, can incorporate cultural awareness of children and their families into the robot design. The results obtained after the co-design process highlight that the co-creation of robotic applications and the robot’s imagery before its actual introduction into activities is fundamental for developing a framework tailored to a specific educational context, for reformulating the project’s prerogatives in such a way that it becomes part of the educational reality, and for giving teachers the opportunity to familiarize themselves with the robot, understand its capabilities, and exploit them according to their educational context.

This work explores a novel approach to empowering robots with visual perception capabilities using textual descriptions. Our approach involves the integration of GPT-4 with dense captioning, enabling robots to perceive and interpret the visual world through detailed text-based descriptions. To assess both user experience and the technical feasibility of this approach, experiments were conducted with human participants interacting with a Pepper robot equipped with visual capabilities. The results affirm the viability of the proposed approach, allowing to perform vision-based conversations effectively, despite processing time limitations.

The synergic orchestration of the cognitive and psychological dimensions characterizes human intelligence. Accordingly, carefully designing this mechanism in artificial intelligence can be a successful strategy to increase human likeness in a robot, enhancing mutual understanding and building a more natural and intuitive interaction. For this purpose, the main contribution of this work is a psychological and cognitive architecture tailored for HRI based on the interplay between robotic personality and memory-based cognitive processes. Indeed, the artificial personality manifests itself not only in various aspects of the behavior but also within the action selection process, which is closely intertwined with personality-dependent hedonic experiences linked to memories. Within this paper, we propose a task- and platform-independent framework, evaluated in a multiparty collaborative scenario. Obtained results show that a robot connected to our proposed framework is perceived as a cognitive agent capable of manifesting perceivable and distinguishable personality traits.

The ROS 2 Navigation Stack (Nav2) has emerged as a widely used software component providing the underlying basis to develop a variety of high-level functionalities. However, when used in outdoor environments such as orchards and vineyards, its functionality is notably limited by the presence of obstacles and/or situations not commonly found in indoor settings. One such example is given by tall grass and weeds that can be safely traversed by a robot, but that can be perceived as obstacles by LiDAR sensors, and then force the robot to take longer paths to avoid them, or abort navigation altogether. To overcome these limitations, domain specific extensions must be developed and integrated into the software pipeline. This paper presents a new, lightweight approach to address this challenge and improve outdoor robot navigation. Leveraging the multi-scale nature of the costmaps supporting Nav2, we developed a system that using a depth camera performs pixel level classification on the images, and in real time injects corrections into the local cost map, thus enabling the robot to traverse areas that would otherwise be avoided by the Nav2. Our approach has been implemented and validated on a Clearpath Husky and we demonstrate that with this extension the robot is able to perform navigation tasks that would be otherwise not practical with the standard components.

This work illustrates the testing of the Spot Robot performed at the training camp of Civil Protection and ANPAS (National Association of Public Assistance) in Foligno. The camp simulates the aftermath of an earthquake with different types of collapsed buildings. We teleoperated the quadruped Spot robot in different areas of the camp where Spot needs to address different challenges. The focus of the testing was not on the objective performance of the robot but on how the robot was subjectively perceived by rescuers of ANPAS and Civil Protection. Initially, we formulated and tested two hypotheses to check if locomotion in some areas is perceived better than in other areas and if there are perceivable differences when the robot is using different types of locomotion gaits. Then, we conducted unstructured interviews with participants who observed the robot in action to describe their rescue procedures and give us suggestions and opinions on what operations they expect the robot might perform.

This study introduces a system implemented on a legged robot, designed to generate a multi-layered map that incorporates semantic information, specifically tailored for Search & Rescue robotics. The article discusses the development of a Machine Learning model based on visual data for recognizing people and environmental features, and its integration into a mapping and navigation architecture. The system was tested in two different locations using the Spot robot by Boston Dynamics, equipped with an external ZED2 depth camera. Tests are described in detail and results analyzed.

Co-speech gesture generation on artificial agents has gained attention recently, mainly when it is based on data-driven models. However, end-to-end methods often fail to generate co-speech gestures related to semantics with specific forms, i.e., Symbolic and Deictic gestures. In this work, we identify which words in a sentence are contextually related to Symbolic and Deictic gestures. Firstly, we appropriately chose 12 gestures recognized by people from the Italian culture, which different humanoid robots can reproduce. Then, we implemented two rule-based algorithms to label sentences with Symbolic and Deictic gestures. The rules depend on the semantic similarity scores computed with the RoBerta model between sentences that heuristically represent gestures and sub-sentences inside an objective sentence that artificial agents have to pronounce. We also implemented a baseline algorithm that assigns gestures without computing similarity scores. Finally, to validate the results, we asked 30 persons to label a set of sentences with Deictic and Symbolic gestures through a Graphical User Interface (GUI), and we compared the labels with the ones produced by our algorithms. For this scope, we computed Average Precision (AP) and Intersection Over Union (IOU) scores, and we evaluated the Average Computational Time (ACT). Our results show that semantic similarity scores are useful for finding Symbolic and Deictic gestures in utterances.

This paper presents a system for diversity-aware autonomous conversation leveraging the capabilities of large language models (LLMs). The system adapts to diverse populations and individuals, considering factors like background, personality, age, gender, and culture. The conversation flow is guided by the structure of the system’s pre-established knowledge base, while LLMs are tasked with various functions, including generating diversity-aware sentences. Achieving diversity-awareness involves providing carefully crafted prompts to the models, incorporating comprehensive information about users, conversation history, contextual details, and specific guidelines. To assess the system’s performance, we conducted both controlled and real-world experiments, measuring a wide range of performance indicators.

The goal-directed behavior observed in humans arises from the intricate interplay of various processes, including personality dynamics, emotional responses to others, memory encoding, the anticipation of future actions, and associated hedonic experiences. Integrating these multiple processes characteristic of human intelligence into a robotic framework aims to enhance the human-likeness of artificial agents and facilitate more natural and intuitive interactions with humans.For this purpose, in this paper, we propose a comprehensive psychological and cognitive architecture where, personality, as it happens for humans, not only influences the execution of actions but also shapes internal reactions to human emotions and guides anticipatory decision-making processes tailored to the agent’s traits. We demonstrate the framework’s effectiveness in generating perceivable synthetic personalities through an experiment involving participants in a dyadic conversation scenario with a digital human, where the digital human’s behavior is driven by its assigned personality. The results show that participants accurately perceive the artificial personality displayed by the digital human. We also demonstrate the potential of our robotic framework to bridge the gap between cognitive and psychological agents, as the findings highlight its ability to create a cognitively and emotionally intelligent digital human.

Educational robotics is increasingly merging into school curricula. Understanding the subjective perceptions of robots, particularly among children, requires nuanced approaches. In this paper, we employed a qualitative coding analysis method to explore how children of different ages conceptualise robots through drawings, and how prior experiences with robotics influence their perceptions. Our findings reveal that the perception of robotics is influenced by cognitive development stages, which is in turn affected by age, and by educational robotics. The latter plays a significant role in shaping perceptions of robots, fostering positive attitudes and aiding cognitive development, particularly in first-grade students. Our insights can inform both teachers to better tailor their educational robotics activities for different age groups and robot designers themselves. For instance, our findings highlight the importance of emotional expression and the preference for humanoid robots among primary school children.

This work describes an immersive control system for a quadruped robot, designed to track the head movements of the operator wearing a virtual reality eye-wear, while also utilizing joystick commands for locomotion control. The article details the implemented closed-loop velocity control approach and the locomotion task specifications. The proposed method has been implemented on a Spot robot from Boston Dynamics, with Meta Quest 2 virtual reality system. Evaluation of the approach involved a user study, where participants engaged in immersive control of the quadruped robot within an indoor experimental environment and provided feedback through standardized questionnaires. Pairwise comparison of the resulting data revealed significant advantages for the proposed immersive control system over a standard remote controller, with enhanced performance observed in the second trial of using the control system. However, participants lacking experience with virtual reality systems reported increased distress symptoms following the experiment.Code: https://www.github.com/aliy98/zed-oculus-spot

The purpose of this research is to study the impact of robot participation in group conversations and assess the effectiveness of different addressing policies. The study involved a total of 300 participants, who were divided into groups of four and engaged in a dialogue with a humanoid robot. The robot acted as a moderator, using information obtained during the conversation to determine which speaker to address. The study found that the policy used by the robot significantly impacted the conversation dynamics. Specifically, the robot provided more balanced attention to each participant and reduced the number of subgroups.

In this work, we propose a strategy to implement the first two steps of the DRABC paradigm (Danger, Response, Airway, Breathing, Circulation) used by rescuers in Search and Rescue (SAR) with the use of a mobile quadruped robot. The robot is programmed to autonomously explore and create a map of the environment with the main objective of identifying areas of danger and reporting them to rescuers (first step of DRABC). While completing this first goal the robot must also identify people still inside the building, mark their position but also evaluate the health state of the person and in particular the response (second step of DRABC). Specifically, we propose new strategies for SAR considering that autonomous behaviour is particularly relevant before the human rescuers arrive: therefore, the policy adopted should privilege covering a broader area in the available time, rather than exploring a smaller area in depth. Strategies have been tested with the Spot robot from Boston Dynamics concerning both exploration and health assessment. The software developed and the tests to validate it are thoroughly described and explained.

The objective of this work is to develop computational cognitive models embedded in a humanoid robot. We focus on Dark Triad constructs and the so-called “Nice and Nasty” Theory of Mind that have never been investigated through a robotic approach. To this end, DT and ToM conceptual models in psychology have been taken as a reference for developing a framework based on the popular PDDL planning language. Next, a cognitive architecture has been implemented on a humanoid robot, with the final objective of making adverse personalities emerge. The motivations of the present work are both theoretical and practical. On the one side, we aim to provide researchers with new insights into DT constructs through simulated and robotic setups. On the other side, we aim to provide a tool to train psychologists to deal with social and antisocial behaviour in a controlled setup. The article includes all the details about the model and the experiments performed.

This article explores the acceptance of a humanoid robot designed to engage in conversations with clinicians and individuals with spinal cord injuries. Building upon prior research, we introduce the concept of “diversity-aware” robots, which possess the capability to interact with people while adapting to their culture, age, gender, preferences, and physical and mental conditions. These robots are connected to a cloud system specifically designed to consider these factors, enabling them to adapt to the context and individuals they interact with. Our experiments involved the NAO robot interacting with both clinicians and individuals with spinal cord injuries in a hospital environment. Subsequent to the interaction, participants completed a questionnaire and underwent an interview. The collected data were analyzed to assess the system’s acceptability and its persistence beyond the initial novelty effect. Furthermore, we investigated whether clinicians exhibited a lower predisposition towards the system and expressed greater concerns than end-users about using the robot, which could potentially hinder the adoption of the system.

Robotic personalities broaden the social dimension of an agent creating feelings of comfort in humans. In this work, we propose a taxonomy model to generate synthetic personalities based on the Big Five model. In particular, this paper describes a generalized framework for artificial personalities whose core is a Bidirectional Encoder Representations from Transformers (BERT) model capable of associating behaviors tailored to each personality trait. The generator is fully integrated within a modular software architecture capable of performing social interaction tasks, being at the same time task-and platform-independent. The proposed framework has been tested in a pilot experiment where human subjects were asked to interact with a humanoid robot displaying different personality traits. Results obtained by the statistical analysis of validated questionnaires show interesting insights about the capability of the framework of generating personalities that are clearly perceived by users, and whose personality dimensions are strongly distinguishable.

This article proposes a new framework to model a scenario in which First Responders, citizens, smart devices, or robots explore the environment in an emergency situation, i.e., after an earthquake, assessing damages and searching for people needing assistance. While moving, the agents observe events and exchange the information collected with other agents encountered: to this end, they use messaging systems purposely adapted to use point-to-point network connections to allow local data exchange between agents even when global network connections are not available. As is common in Delay Tolerant Networks, exchanged messages are locally stored: when a global network is available, the agents can upload all the information collected by themselves and other agents they encountered to a Control Room or a database in the Cloud. Differently from traditional DTN algorithms such as Epidemic and Spray&Wait, we propose a solution that keeps track of agents that shared information along the path and assess the quality of the information collected by multiple agents through a reputation-based mechanism that is safer than majority voting. A simulator compatible with OpenStreetMap is presented, as well as simulated experiments in two Italian towns to validate the feasibility of the approach.

Emotions have always played a crucial role in human evolution, improving not only social contact but also their ability to adapt and react to a changing environment. In the field of social robotics, providing robots with the ability to recognize human emotions through the interpretation of non-verbal signals may represent the key to more effective and engaging interaction. However, the problem of emotion recognition has usually been addressed in limited and static scenarios, by classifying emotions using sensory data such as facial expressions, body postures, and voice. This work proposes a novel emotion recognition framework, based on the appraisal theory of emotion. According to the theory, the expected person’s appraisal of a given situation depending on their needs and goals (henceforth referred to as "appraisal information") is combined with sensory data. A pilot experiment was designed and conducted: participants were involved in spontaneous verbal interaction with the humanoid robot Pepper, programmed to elicit different emotions in various moments. Then, a Random Forest classifier was trained to classify positive and negative emotions using: (i) sensor data only; (ii) sensor data supplemented by appraisal information. Preliminary results confirm a performance improvement in emotion classification when appraisal information is considered.

Context-dependent meaning recognition in natural language utterances is one of the key problems of computational pragmatics. Abductive reasoning seems apt for modeling and understanding these phenomena. In fact, it presents observations through hypotheses, allowing us to understand subtexts and implied meanings without exact deductions. For this reason in this paper, we are going to explore abductive reasoning and context modeling in human-robot interaction. Rather than a radical inferential approach, we assumed a conventional approach towards context-depending meanings, i.e, they are conventionally encoded rather than inferred from the utterances. In order to address the problem, a case study is presented, analyzing whether such a system could manage correctly these linguistic phenomena. The results obtained confirm the validity of a conventional approach in context modeling and, on this basis, further models are proposed to work around the limitations of the case study.

Achieving natural and engaging verbal interactions is one of the main challenges faced by Social Robotics. In this context, physical embodiment may be one of the most critical factors: indeed, previous work indicates that physical robots elicit more favorable social responses than virtual agents. However, the effects of physical embodiment have been analysed only in some specific and limited scenarios, where verbal interaction was reduced to basic commands.The current work aims at investigating the effect of robots' physical embodiment in a pure conversation task, by considering some relevant aspects of social interaction, such as usability, speech interface quality, user satisfaction and engagement. To this aim, a pilot experiment where participants were required to chitchat with a robot and a smartphone app, both connected to the same conversation framework, has been carried out. Preliminary results are presented and discussed, and they offer interesting insights about the positive effects of physical embodiment on some of the analysed aspects.

Unmanned Aerial Vehicles (UAVs) popularity is increased in recent years, and the domain of application of this new technology is continuously expanding. However, although UAVs may be extremely useful in monitoring contexts, the operational aspects of drone patrolling services have not yet been extensively studied. Specifically, patrolling and inspecting with UAVs different targets distributed over a large area is still an open problem, due to battery constraints and other practical limitations. In this work, we propose a deterministic algorithm for patrolling large areas in a pre- or post-critical event scenario. The autonomy range of UAVs is extended with the concept of Social Drone Sharing: citizens may offer their availability to take care of the UAV if it lands in their private area, being thus strictly involved in the monitoring process. The proposed approach aims at finding optimal routes in this context, minimizing the patrolling time and respecting the battery constraints. Simulation experiments have been conducted, giving some insights about the performance of the proposed method.

Personality is one of the most important factors in human interactions, which retains its importance in human- robot interactions with social robots. This work focusses on the varied linguistic strategies which characterize the personality traits of extraversion and introversion, analysing the main features that differentiate both personalities and eventually proposing a verbal communication model for the extraverted and introverted personality of a conversational social robot. The model classifies and converts phrases, with the result of building different communication styles. A pilot study, involving human subjects and the humanoid robot Pepper, programmed to mimic both extraverted and introverted personality types, has been conducted, with the twofold aim of assessing if differences between the two personalities of the robot can be perceived, and analyzing the effects of different personality traits on verbal interaction with human subjects. Preliminary results seem to confirm the law of attraction in human-human interaction for the extraverted personality.

The video describes the novel concept of “culturally competent robotics”, which is the main focus of the project CARESSES (Culturally-Aware Robots and Environmental Sensor Systems for Elderly Support). CARESSES a multidisciplinary project whose goal is to design the first socially assistive robots that can adapt to the culture of the older people they are taking care of. Socially assistive robots are required to help the users in many ways including reminding them to take their medication, encouraging them to keep active, helping them keep in touch with family and friends. The video describes a new generation of robots that will perform their actions with attention to the older person's customs, cultural practices and individual preferences.

The article focusses on the work performed in preparation for an experimental trial aimed at evaluating the impact of a culturally competent robot for care home assistance. Indeed, it has been estabilished that the user's cultural identity plays an important role during the interaction with a robotic system and cultural competence may be one of the key elements for increasing capabilities of socially assistive robots. Specifically, the paper describes part of the work carried out for the definition and implementation of two different robotic systems for the care of older adults: a culturally competent robot, that shows its awareness of the user's cultural identity, and a reference robot, non culturally competent, but with the same functionalities of the former. The design of both robots is here described in detail, together with the key elements that make a socially assistive robot culturally competent, which should be absent in the non-culturally competent counterpart. Examples of the experimental phase of the CARESSES project, with a fictional user are reported, giving a hint of the validness of the proposed approach.

Research focusing on the development of socially assistive robots (SARs) for the care of older adults has grown in recent years, prompting a great deal of ethical analysis and reflection on the future of SARs in caring roles. Much of this ethical thinking, however, has taken place far from the settings where technological innovation is practiced. Different frameworks have been proposed to bridge this gap and enable researchers to handle the ethical dimension of technology from within the design and development process, including Value Sensitive Design (VSD). VSD has been defined as a “theoretically grounded approach to the design of technology that accounts for human values in a principled and comprehensive manner throughout the design process”. Inspired in part by VSD, we have developed a process geared towards embedding ethics at the core of CARESSES, an international multidisciplinary project that aims to design the first culturally competent SAR for the care of older adults. Here we describe that process, which included extracting key ethical concepts from relevant ethical guidelines and applying those concepts to scenarios that describe how the CARESSES robot will interact with individuals belonging to different cultures. This approach highlights the ethical implications of the robot's behavior early in the design process, thus enabling researchers to identify and engage with ethical problems proactively.

The way in which humans behave, speak and interact is deeply influenced by their culture. For example, greeting is done differently in France, in Sweden or in Japan; and the average interpersonal distance changes from one cultural group to the other. In order to successfully coexist with humans, robots should also adapt their behavior to the culture, customs and manners of the persons they interact with. In this paper, we deal with an important ingredient of cultural adaptation: how to generate robot plans that respect given cultural preferences, and how to execute them in a way that is sensitive to those preferences. We present initial results in this direction in the context of the CARESSES project, a joint EU-Japan effort to build culturally competent assistive robots.

The functionalities and behaviours of socially assistive robots for the care of older people are usually defined by the robot's designers with limited room for runtime adaptation to meet the preferences, expectations and needs of the assisted person. However, adaptation plays a crucial role for the robot's acceptability and ultimately for its effectiveness. Culture, which deeply influences a person's preferences and habits, can be viewed as an invaluable “enabling technology” to achieve such level of adaptation. This paper discusses how guidelines describing culturally competent assistive behaviours can be encoded in a robot to effectively tune its actions, gestures and words. The proposed system is implemented on a Pepper robot and tested with an Indian persona, whose habits and preferences the robot discovers and adapts to at runtime.

Cultural adaptation, i.e., the matching of a robot's behaviours to the cultural norms and preferences of its user, is a well known key requirement for the success of any assistive application. However, culture-dependent robot behaviours are often implicitly set by designers, thus not allowing for an easy and automatic adaptation to different cultures. This paper presents a method for the design of culture-aware robots, that can automatically adapt their behaviour to conform to a given culture. We propose a mapping from cultural factors to related parameters of robot behaviours which relies on linguistic variables to encode heterogeneous cultural factors in a uniform formalism, and on fuzzy rules to encode qualitative relations among multiple variables. We illustrate the approach in two practical case studies.

Cultural competence is a well known requirement for an effective healthcare, widely investigated in the nursing literature. We claim that personal assistive robots should likewise be culturally competent, aware of general cultural characteristics and of the different forms they take in different individuals, and sensitive to cultural differences while perceiving, reasoning, and acting. Drawing inspiration from existing guidelines for culturally competent healthcare and the state-of-the-art in culturally competent robotics, we identify the key robot capabilities which enable culturally competent behaviours and discuss methodologies for their development and evaluation.

The rapidly increasing number of elderly people has led to the development of in-home assistive robots for assisting and monitoring elderly people in their daily life. To these ends, indoor scene and human activity recognition is fundamental. However, image processing is an expensive process, in computational, energy, storage and pricing terms, which can be problematic for consumer robots. For this reason, we propose the use of computer vision cloud services and a Naive Bayes model to perform indoor scene and human daily activity recognition. We implement the developed method on the telepresence robot Double to make it autonomously find and approach the person in the environment as well as detect the performed activity.

Wheeled Robots (WRs) are widely used in many different contexts, and usually they are required to operate in partial or total autonomy. In particular, in a wide range of situations, having the capability of following a predetermined path and avoiding unexpected obstacles can be extremely relevant. On these basis, this paper analyzes an integrated approach for path following and obstacle avoidance applied to unicycle-type robots. The approach is based on the definition of the path to be followed as a curve f(x, y) in space, while obstacles are modeled as Gaussian functions that modify the original function, generating a resulting safe path. The attractiveness of this methodology which makes it look very simple, is that it neither requires the computation of a projection of the robot position on the path, nor does it need to consider a moving virtual target to be tracked. The performances of the proposed approach are analyzed by means of a series of experiments performed in dynamic environments with unicycle-type robots.

The article proposes a solution allowing a multicopter to generate and follow a path while taking into account the obstacles in the environment. Specifically, we introduce a method for path definition that describes a curve as the intersection of two surfaces. Then, the article proposes a computationally efficient strategy allowing to modify either surface, and hence the resulting path, to take into account the presence of obstacles perceived in real-time. The algorithm has been implemented and embedded in a software package to control the flight of a fully autonomous AscTec Firefly hexacopter with two cameras and onboard processing capabilities.

This paper proposes an integrated path following and obstacle avoidance framework for robots in crowded environments. The architecture considers two major requirements: (i) a given performance level for path following must be guaranteed; (ii) human-friendly robots must behave predictably and naturally, in order to avoid dangerous people reactions. In order to generate paths perceived as natural, and inspired by what happens in high traffic highways, we assume a path to consist of a family of curves, which can be switched using sensory information. The resulting behaviour is both predictable (paths are planned and validated beforehand) and natural (the overall qualitative behaviour is maintained when switching curves). The overall error is upper bounded by the curves configuration. The paper presents results in simulation.

Human Odometry (HO) is the process of providing a person with a continuous estimate of their location, on the basis of information acquired solely by sensors carried around by the person themselves. In an effort towards the development of effective and robust HO systems, we present the Human Odometry Outdoor Dataset (HOOD), a public collection of labelled accelerometer and gyroscope data recordings. We compare four sensor placements (foot, waist, wrist, chest) to identify the most suitable placement for different types of motions (ranging from walking to slithering), occurring in highly diverse real environments (such as flat grass fields, staircases and rough terrains).

Robots for the elderly are a particular category of home assistive robots, helping people in the execution of daily life tasks to extend their independent life. Such robots should be able to determine the level of independence of the user and track its evolution over time, to adapt the assistance to the person capabilities and needs. Human Activity Recognition systems employ various sensing strategies, relying on environmental or wearable sensors, to recognize the daily life activities which provide insights on the health status of a person. The main contribution of the article is the design of an heterogeneous information management framework, allowing for the description of a wide variety of human activities in terms of multi-modal environmental and wearable sensing data and providing accurate knowledge about the user activity to any assistive robot.

A common way to organize a high number of robots, both when moving autonomously and when controlled by a human operator, is to let them move in formation. This is a principle that takes inspiration from the nature, that maximizes the possibility of monitoring the environment and therefore of anticipating risks and finding targets. In robotics, alongside these reasons, the organization of a robot team in a formation allows a human operator to deal with a high number of agents in a simpler way, moving the swarm as a single entity. In this context, the typology of visual feedback is fundamental for a correct situational awareness, but in common practice having an optimal camera configuration is not always possible. Usually human operators use cameras on board the multirotors, with an egocentric point of view, while it is known that in mobile robotics overall awareness and pattern recognition are optimized by exocentric views. In this article we present an analysis of the performance achieved by human operators controlling a swarm of UAVs in formation, accomplishing different tasks and using different point of views. The control architecture is implemented in a ROS framework and interfaced with a 3D simulation environment. Experimental tests show a degradation of performance while using egocentric cameras with respect of an exocentric point of view, although cameras on board the robots allow to satisfactorily accomplish simple tasks.

The automatic monitoring of specific Activities of Daily Living (ADL) can be a useful tool for Human-Robot Interaction in smart environments and Assistive Robotics applications. The qualitative definition that is given for most ADL and the lack of well-defined benchmarks, however, are obstacles toward the identification of the most effective monitoring approaches for different tasks. The contribution of the article is two-fold: (i) we propose a taxonomy of ADL allowing for their categorization with respect to the most suitable monitoring approach; (ii) we present a freely available dataset of acceleration data, coming from a wrist-worn wearable device, targeting the recognition of 14 different human activities.

The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

It is well known that there is a worldwide increase in both the number of elderly people and the number of elderly people with mild cognitive impairments [1], [2] and thus in need of assistance in the execution of activities of daily living. Socially Assistive Robotics is a novel research field, aiming at the design of robots relying on social means to interact with people and with a well-defined assistive purpose. The contribution of the article is three-fold: (i) a detailed analysis of the requirements of a socially assistive robot helping elderly people in the execution of everyday activities; (ii) the outline of the design principles for socially assistive robots; (iii) a first proposal for a wearable robot able to engage humans at the cognitive level.

This paper describes a system for context awareness in smart environments, which is based on an ontology expressed in description logic and implemented in OWL 2 EL, which is a subset of the Web Ontology Language that allows for reasoning in polynomial time. The approach is different from all other works in the literature since the proposed system requires only the basic reasoning mechanisms of description logic, i.e., subsumption and instance checking, without any additional external reasoning engine. Experiments performed with data collected in three different scenarios are described, i.e., the CASAS Project at Washington State University, the assisted living facility Villa Basilea in Genoa, and the Merry Porter mobile robot at the Polyclinic of Modena.

This paper introduces semantic-aware real-time (SeART), an extension to conventional operating systems, which deals with complex real-time robotics applications. SeART addresses the problem of selecting a subset of tasks to be scheduled depending on the current operating context: mission objectives, other tasks currently executed, the availability or unavailability of sensors and other resources, as well as temporal constraints. Toward this end, SeART is able to represent the semantics of tasks to be scheduled, i.e., what tasks are meant for, and to use this information in the scheduling process. This paper describes in detail the SeART architecture by focusing on representations and reasoning procedures, and presenting a case-study which is related to mobile robotics for autonomous objects transportation.

The article describes a system for describing and recognizing spatial and temporal patterns of events. The system is based on an ontology described through the Description Logics formalism and implemented in OWL DL. The approach is different from all other works in the literature since the system does not require an external reasoning engine, but relies only on the base mechanism for ontology classification. Experiments performed in two different scenarios are described, i.e., a Smart Home and a mobile robot for autonomous transportation operating within a partially automated building.

Humanoid robots operating in the real world must exhibit very complex behaviors, such as object manipulation or interaction with people. Such capabilities pose the problem of being able to reason on a huge number of different objects, places and actions to carry out, each one relevant for achieving robot goals. This article proposes a functional representation of objects, places and actions described in terms of affordances and capabilities. Everyday problems can be efficiently dealt with by decomposing the reasoning process in two phases, namely problem awareness (which is the focus of this article) and action selection.

The article describes a tool for the fast prototyping and deployment of context-aware applications, in particular to welcome visitors in urban areas. The system has been conceived to guarantee continuous access to everybody, everywhere, at any time, and therefore it does not rely on any special device to connect visitors to the intelligent environment. In fact, we assume that visitors are equipped with low-end mobile phones embedded with bluetooth technology, which provide approximate positioning information. In spite of this, the system must be able to assess the current context in terms of user location, preferences, current activity, and to suggest city-tours and activities which meets the most the visitor's expectations. The article shows how, basing on Google maps API and OWLDL ontologies, the rapid prototyping and rapid deployment of outdoor context-aware applications based on bluetooth messaging and positioning information can be achieved.

This article introduces a model for representing motion primitives for entertainment humanoid robots using Generalized Hierarchical AND/OR graphs. On the one hand, the goal is to drive robots with scripts, as if they were on a stage. On the other hand, the approach allows for storing a minimum amount of behaviours, thereby reducing on-board memory and computational requirements. Standard ontology-based reasoning mechanisms are used to operate on such a representation, in a fully hierarchical fashion. Experimental validation has been assessed using toy Kondo robots.

This paper describes an algorithm, which has been specifically designed to solve the problem of multirobot-controlled frequency coverage (MRCFC), in which a team of robots are requested to repeatedly visit a set of predefined locations of the environment according to a specified frequency distribution. The algorithm has low requirements in terms of computational power, does not require inter-robot communication, and can even be implemented on memoryless robots. Moreover, it has proven to be statistically complete as well as easily implementable on real, marketable robot swarms for real-world applications.

A new feedback control model is provided that allows a wheeled vehicle to follow a prescribed path. Differently from all other methods in the literature, the method that is proposed neither requires the computation of a projection of the robot position on the path, nor does it need to consider a moving virtual target to be tracked. Nevertheless, it guarantees asymptotic convergence to a generic 2-D curve which can be represented through its implicit equation in the formf(x,y)=0, and it puts no bounds on the initial position of the vehicle, provided that ∇f≠ 0 .

The focus of the present work is on daily activity planning, i.e., representations and algorithms able to produce a course of action to deal efficiently with problems of daily living. To achieve this, the article proposes a functional representation of everyday objects, places and actions described in terms of affordances. Its contributions are two--fold: (i) it proposes to represent affordances and capabilities as regions in a proper affordance and capability space, and to describe such regions using neural maps; (ii) it proposes to decompose the planning process into different activities, by introducing a phase referred to as Problem Awareness preceding Action Planning, which allows to reduce the planning space in order to tackle large-scale planning problems.

The article proposes a feedback control system for path following in presence of obstacles that is an extension of previous work and is made of two components: (i) a sensor-based, real-time model that generates and periodically updates the path on-line in order to avoid both known and unforeseen obstacles, and (ii) a feedback-control model that is capable of driving a unicycle vehicle along the collision free path. The system has some unique characteristics, among which it requires very few computational resources as a consequence of its extreme simplicity.

The article proposes a new feedback control model which is suited for path following in a 3 Dimensional Cartesian space. Differently from other methods in literature, the method proposed neither requires to compute a projection of the robot's position on the path, nor it needs considering a moving virtual target. In spite of this: i) it guarantees asymptotic stability for every 3D curve which can be represented through a couple of intersecting surfaces f1(X, Y, Z) = 0, f2(X, Y, Z) = 0; ii) it does not put any bounds on the initial position of the vehicle depending on the path's curvature.

This paper presents an architecture for context-aware Ubiquitous Robotics applications, where mobile robots cooperate with intelligent environments to fulfill their tasks. Specifically, the work is focused on distributed knowledge representation issues and context assessment strategies, and introduces a technique for on-line context recognition in highly dynamic environments. Experimental validation, performed in a civillian hospital building, is described and discussed.

A knowledge representation system is introduced that allows the recognition of temporal patterns of events in context-aware environments. The system is based on standard frameworks, such as an ontology, an inference mechanism, and relational operators acting on numerical quantities. The paper describes how knowledge is managed, then introduces a collection of temporal operators that are inspired by the Allen's interval algebra, and then details a situation recognition algorithm to assess knowledge semantics. An example is reported to describe the approach.

The article proposes a feedback control system for real-time navigation and obstacle avoidance that is made of two components: (i) a sensor-based, real-time model that generates and periodically updates the path on-line in order to avoid both known and unforeseen obstacles, and (ii) a feedback-control model that is capable of driving a unicycle vehicle along the collision free path. The system has some unique characteristics, among which it requires very few computational resources as a consequence of its extreme simplicity. In spite of this, it is formally demonstrated to be asymptotically stable, as well as computationally efficient to be implemented in real-world scenarios where obstacles are not known, and possibly move in the environment.

The article proposes a new feedback control model that allows to track a generic curve in the Cartesian Space expressed through its implicit equation, and has minimal requirements in terms of measurement and computation capabilities. The model measures only the distance between the vehicle and the path, whereas it ignores the vehicle's orientation. In spite of this, it allows to regulate to zero both the distance to the path and the difference between the vehicle's orientation and the tangent to the curve, and it is asymptotically stable.

This paper presents muNav, a novel approach to navigation which, with minimal requirements in terms of onboard sensory, memory, and computational power, exhibits way-finding behaviors in very complex environments. The algorithm is intrinsically robust, since it does not require any internal geometrical representation or self-localization capabilities. Experimental results, performed with both simulated and real robots, validate the proposed theoretical approach.

A framework is introduced that is aimed at integrating ontology and logic approaches for context-awareness, suitable for use in Ambient Intelligence (AmI) scenarios. In particular, the context description language CDL is described, which allows to easily specify patterns of events which occurrences must be monitored by actual systems. As long as systems evolve, symbolic data originating from heterogeneous sources are first aggregated and then classified according to formulas described in CDL. Experimental results performed in a Smart Home environment are presented and discussed.

This paper describes ANSER, a system designed to perform surveillance in civilian airports and similar wide outdoor areas. Whereas an intelligent system - possibly controlled by a human supervisor - is able to integrate the information originating from different sources (i.e., fixed devices and sensors distributed throughout the environment) and to coordinate their behaviors in case of anomalies, the mobile robot is a significant part of the overall system: its main subsystems, i.e., autonomous surveillance, localization (performed using only a non-differential GPS and a laser rangefinder) and navigation are investigated in depth. Experimental results validate the robustness and reliability of the approach.

The paper focuses on the localization subsystem of ANSER, a mobile robot for autonomous surveillance in civilian airports and similar wide outdoor areas. ANSER localization subsystem is composed of a non-differential GPS unit and a laser rangefinder for landmark-based localization (inertial sensors are absent). An augmented state vector approach and an Extended Kalman filter are successfully employed to estimate the colored components in GPS noise, thus getting closer to the conditions for the EKF to be applicable.

The paper deals with the role of line features in mobile robot self-localization, when an extended Kalman filter is adopted for position tracking. First, a theoretical analysis is introduced, showing how the "length" of each extracted line (i.e., the number of the contributing range measurements) affects the localization accuracy. Second, a novel approach that takes into account the main findings of the theoretical analysis is considered. Finally, experimental results are used to validate the system.

This paper presents a novel navigation approach which, with minimal requirements in terms of on-board sensory, memory, and computational power, exhibits way-finding behaviors in very complex environments. The algorithm does not require any internal spatial representation, nor self-localization abilities: however, since it relies on heuristics to find a path to the goal, completeness is not guaranteed. The paper shows that this is the price to pay for augmenting the robustness of the system in presence of incomplete information and measurement noise

We propose a multiagent, distributed approach to autonomous mobile robotics which is an alternative to most existing systems in literature: robots are thought of as mobile units within an intelligent environment where they coexist and co-operate with fixed, intelligent devices that are assigned different roles: helping the robot to localize itself, controlling automated doors and elevators, detecting emergency situations, etc. To achieve this, intelligent sensors and actuators (i.e. physical agents) are distributed both onboard the robot and throughout the environment, and they are handled by Real-Time software agents which exchange information on a distributed message board. The paper outlines the benefits of the approach in terms of efficiency and Real-Time responsiveness.

In this paper we present a mobile system for visual tracking, i.e., a mobile platform equipped with a TV camera, which is capable of following a human leader along complex trajectories. Differently from existing systems, which mainly rely on the detection of colour blobs or particular features/markers, our system is based on the detection of motion through optical flow computation, thus being implicitly able to follow both persons and other robots with different characteristics in terms of colour, shape, etc.

Psychologists' debates on the role of knowledge to control actions in living beings have strongly influenced the research in the field of artificial intelligence and, consequently, of robotics. In both fields a focus of the debate has been the relevance (or even the presence) of a mental/internal representation in driving the course of actions of biological/artificial beings. In this paper we show that even very complex navigation tasks within maze-like environments can be carried out by an agent which needs to store just 'a handful of bytes' of internal representation about the world in which it is moving. The approach is called micronavigation, since it aims to capture the problem of mobile robot navigation in its entirety but with a minimalist approach.

We address the problem of autonomous navigation and localization in indoor environments, referring in particular to the specific scenario of service mobile robotics applications. The localization system uses active beacons (i.e. active transponders distributed throughout the building) as reference points; the estimate of the position of the robot and its uncertainty, both retrieved by correcting the estimate provided by odometry through an extended Kalman filter, are fed to the navigation system in order to help the robot to plan and execute target-oriented navigation tasks while showing a reactive behavior to handle the unpredictability of the environment.

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.

Autonomous and semi-autonomous mobile robots have to perform a multiplicity of concurrent activities in order to carry out useful tasks in unstructured human-populated environments. Even if it is commonly accepted that a successful accomplishment of assigned tasks requires some sort of real time capability to quickly react and adapt to environmental changes, it is not clear which operating system support is best suited for the scheduling and synchronizing of concurrent activities with different timing requirements. This paper discusses this problem, comparing two different real time scheduling policies for autonomous robot applications: pre-emptive rate monotonic and non pre-emptive Earliest Deadline First (EDF). Experimental results are presented and evaluated.

Autonomous mobile robots have to perform a multiplicity of concurrent activities to carry out useful tasks while quickly reacting to sensorial inputs in a dynamic, partially unknown environment. The paper discusses the operating system requirements of a mobile robotic system, by focusing on the timing and communication requirements of the involved tasks. A distributed software architecture is proposed which implements a hybrid (pre-emptive/non-pre-emptive) task scheduling policy and a dedicated inter-task communication protocol, offering an efficient programming interface for the development of soft real-time robotic applications.

In this paper we propose a distributed architecture for intelligent robotic systems. The architecture is specific to this domain because it intends to provide support for the type of applications that share particular functional requirements: concurrent perception and action, task and plan execution, reasoning, "intelligent behaviours". The architecture also aims to improve re-usability and integration of different software components. It allows the transparent distribution of processes on different computers in a network to take advantage of the increased computational power and so overcome the vehicles on-board limitations. We focus on the related cognitive model on the internal structure of the architecture and of its components. We also examine in detail the EIE protocol we defined to exchange information within the distributed system. Finally we indicate some experimental results.