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International audience; Creating a convincing affective robot behavior is a challenging task. In this paper, we are trying to coordinate between different modalities of communication: speech, facial expressions, and gestures to make the robot interact with human users in an expressive manner. The proposed system employs videos to induce target emotions in the participants so as to start interactive discussions between each participant and the robot around the content of each video. During each experiment of interaction, the expressive ALICE robot generates an adapted multimodal behavior to the affective content of the video, and the participant evaluates its characteristics at the end of the experiment. This study discusses the multimodality of the robot behavior and its positive effect on the clarity of the emotional content of interaction. Moreover, it provides personality and gender-based evaluations of the emotional ex-pressivity of the generated behavior so as to investigate the way it was perceived by the introverted-extroverted and male-female participants within a human-robot interaction context.

International audience; Most of automotive researches are focused on autonomous vehicles. Both industrials and academics are working on several changes, and the way to coordinate the increasing number of chassis systems is one among them. This paper proposes a more suitable control architecture for autonomous vehicles complexity. This architecture is based on control allocation techniques developed especially to handle over-actuated systems. The allocation is made by taking into account the tire dynamics couplings through a new linear tire model with varying parameters. Simulations of combined manoeuvres proved the potential of this architecture and its relevance for future autonomous vehicles.

International audience; This special issue aims at examining and promoting recent developments in the Assistive/Social Robotics field and future directions including the related challenges and how these can be overcome with particular focus on computational intelligence methodologies. With robots being integrated more and more in our environments and daily activities, the effectiveness of robotics applications has to rely on robots' ability to perceive, reason, and adapt on-the-fly to the users' behavior and needs. In particular, the development of personal robots, as assistive technological tools, challenges researchers to develop socially intelligent and adaptive robots that can collaborate with people in real environment. This special issue is comprised of 14 papers, fostering discussions and investigations in robot behavior adaptation and learning, in socially assistive robotics, and in developing reliable systems for natural interactions between humans and robots. Several aspects and perspectives are taken into consideration by the authors covering various topics such as adaptability during real-world human-robot interactions ; new reinforcement learning and learning by teaching techniques especially used in the context of child-robot interaction ; social robot responses, perception and adaptation by means of a semantic representation; robots acceptability and security. Furthermore, several assistive applications for the elderly, children, and people are presented. B Mariacarla Staffa

Research related to regulatory focus theory has shown that the way in which a message is conveyed can increase the effectiveness of the message. While different research fields have used this theory, in human-robot interaction (HRI), no real attention has been given to this theory. In this paper, we investigate it in an in the wild scenario. More specifically, we are interested in how individuals react when a robot suddenly appears at their office doors. Will they interact with it or will they ignore it? We report the results from our experimental study in which the robot approaches 42 individuals. Twenty-nine of them interacted with the robot, while the others either ignored it or avoided any interaction with it. The robot displayed two types of behavior (i.e., promotion or prevention). Our results show that individuals that interacted with a robot that matched their regulatory focus type interacted with it significantly longer than individuals that did not experience regulatory fit. Other qualitative results are also reported, together with some reactions from the participants.

International audience; We design a personalized human-robot environment for social learning for individuals with autism spectrum disorders (ASD). In order to define an individual�s profile, we posit that the individual�s reliance on proprioceptive and kinematic visual cues should affect the way the individual suffering from ASD interacts with a social agent (human/robot/virtual agent). In this paper, we assess the potential link between recognition performances of body/facial expressions of emotion of increasing complexity, emotion recognition on platforms with different visual features (two mini-humanoid robots, a virtual agent, and a human), and proprioceptive and visual cues integration of an individual. First, we describe the design of the EMBODI-EMO database containing videos of controlled body/facial expressions of emotions from various platforms. We explain how we validated this database with typically developed (TD) individuals. Then, we investigate the relationship between emotion recognition and proprioceptive and visual profiles of TD individuals and individuals with ASD. For TD individuals, our results indicate a relationship between profiles and emotion recognition. As expected, we show that TD individuals that rely more heavily on visual cues yield better recognition scores. However, we found that TD individuals relying on proprioception have better recognition scores, going against our hypothesis. Finally, participants with ASD relying more heavily on proprioceptive cues have lower emotion recognition scores on all conditions than participants relying on visual cues.

International audience; Many research projects are focusing on automated vehicles and reinventing the automotive industry. New technologies are introduced, and the pillars of the ground vehicle are revisited and new challenges appeared. This paper focuses on the chassis systems and their control strategies. While assistance strategies use to manage specific operations, autonomous vehicles need to handle simultaneous operations. Optimal solutions for systems coordination are discussed in this paper. Results show that the more we get closer to full autonomous driving where an important number of embedded systems is needed, the more optimal coordination strategies are relevant. These strategies should be considered in the upcoming automotive industrial standards.

Our previous work in detecting deception in HRI was based on research findings from the psychology of inter-human interactions. Nonetheless, these conclusions may or may not be directly applied in HRI, as humans may not behave similarly when deceiving a robot. This paper studies the differences between human physiological manifestations during a deception card game scenario when playing it with a human or a robot partner. Our results show the existence of significant differences between the participants’ skin conductance, eye openness, and head pose when playing the game with a robot partner compared to when playing the game with a human partner. These results will then be used to improve the ability of robots to detect deception in HRI.

For socially assistive robots, being able to recognize basic human actions is an important capability. The sensors, which are frequently mounted on most recent robots, such as RGB-D and thermal cameras, as well as the advances in deep learning have enabled the research on activity recognition to grow. In this paper, we collected our own dataset of actions in a home-like scenario, which contains thermal imagery in addition to RGB-D data and we proposed a method based on Long-term Recurrent Convolutional Networks (LRCN). We showed that our method has an accuracy comparable with the state-of-the-art. We also proved that thermal information can improve the recognition accuracy. Furthermore, we tested the real-time capability of our system and conducted a real-time experiment with a robot (Pepper robot from Softbank Robotics) so as to investigate the effect of a robot enabled with action recognition capability in a human-robot interaction.

Social robots need to understand human activities, dynamics, and the intentions behind their behaviors. Most of the time, this implies the modeling of the whole scene. The recognition of the activities and intentions of a person are inferred from the perception of the individual, but also from their interactions with the rest of the environment (i.e., objects and/or people). Centering on the social nature of the person, robots need to understand human social cues, which include verbal but also nonverbal behavioral signals such as actions, gestures, body postures, facial emotions, and proxemics. The correct understanding of these signals helps these robots to anticipate the needs and expectations of people. It also avoids abrupt changes on the human–robot interaction, as the temporal dynamics of interactions are anchored and driven by a major repertoire of social landmarks. Within the general framework of interaction of robots with their human counterparts, this paper reviews recent approaches for recognizing human activities, but also for perceiving social signals emanated from a person or a group of people during an interaction. The perception of visual and/or audio signals allow them to correctly localize themselves with respect to humans from the environment while also navigating and/or interacting with a person or a group of people.

International audience; In this paper, we describe the elaboration and the validation of a body and face database of 96 videos of 1 to 2 seconds of duration, expressing 4 emotions (i.e., anger, happiness, fear, and sadness) elicited through 4 platforms of increased visual complexity and level of embodiment. The final aim of this database is to develop an individualized training program designed for individuals suffering of autism in order to help them recognize various emotions on different test platforms: two robots, a virtual agent, and a human. Before assessing the recognition capabilities of individuals with ASD, we validated our video database on typically developed individuals (TD). Moreover, we also looked at the relationship between the recognition rate and their personality traits (extroverted (EX) vs. introverted (IN)). We found that the personality of our TD participants didn’t lead to a different recognition behavior. However, introverted individuals better recognized emotions from less visually complex characters than extroverted individuals.

A survey of the recent literature on user profiling and behavioral adaptation in human-robot interaction is presented.Approaches are categorized with respect to being mainly related to the physical, cognitive or social aspects of the interaction.Key themes are highlighted to provide directions for future research. To effectively collaborate with people, robots are expected to detect and profile the users they are interacting with, but also to modify and adapt their behavior according to the learned models. The goal of this survey is to focus on the perspective of user profiling and behavioral adaptation. On the one hand, human-robot interaction requires a human-oriented perception to model and recognize the human actions and capabilities, the intentions and goals behind such actions, and the parameters characterizing the social interaction. On the other hand, the robot behavior should be adapted in its physical movement within the space, in the actions to be selected to achieve collaboration, and by modulating the parameters characterizing the interaction. In this direction, this survey of the current literature introduces a general classification scheme for both the profiling and the behavioral adaptation research topics in terms of physical, cognitive, and social interaction viewpoints.

In order to promote reuse of software in robotics, standardization, benchmarking, and formalization activities in robotics are being undertaken by many technical working groups and independent agencies. Absence of integrated tools is the real barrier that exists between early adopters of such efforts and early majority of research and industrial community. In this paper, we provide a detailed discussion on how frameworks for designing robotic systems can be formally specified and developed using our meta-framework - SafeRobots. We have demonstrated this process using a mobile robot case study.

This workshop aims at advancing the topic of the relationships between robotics science and experiments in HRI. The full-day workshop follows on previous events in interdisciplinary venues focusing on efforts to establish reproducible, standardized experiments in order to adequately benchmark and evaluate human-robot interaction, scientific achievements and results in the areas of industrial, medical, social, service and personal-care robots. The words "standard" and "standardized" sometimes are misunderstood as fixing and blocking the freedom of the researcher to create new experiments and therefore to advance the scientific knowledge. As per this workshop, the words must be intended as a "way of experimenting temporarily agreed by the community, that is evolving and tracking the scientific progress through continuous revision by the community itself".

Our work aims at developing a behavioral model enabling customized and natural interaction between a robot and an elderly individual in the context of the EU H2020 ENRICHME Project - Grant Agreement 643691. The main idea of the project is to make the robot able to learn from past experiences and to adapt its behavior to the profile (i.e., personality, cognitive disability level, emotional internal states) and preferences of the person it interacts with. In this paper the robot will try to determine if the person it interacts with is trying to deceive it or not. Some first preliminary experiments and results are presented.

In social HRI context, the robot’s usefulness and appropriate behavior plays an important role. A robot should be able to understand the human’s internal state (i.e., physiological and psychological states) so as to provide an adaptive and thus efficient assistance within daily life activities. Measuring stress and frustration of an individual while performing a certain task is a critical element that can help the robot adapt its behavior so as to improve user’s interest and task performance and to reduce his/her frustration. In this paper, we designed an experiment called “Stress Game”. In our work, stress is measured in terms of heart rate signal. The robot displays different behaviors as a function of user’s personality and game condition. We conducted our experiments with the NAO robot. The experimental results support our hypotheses that the robot has a positive effect on stress relief.

International audience; Robots have increasingly been used for improving the quality of life of people with special needs, such as people suffering from Mild Cognitive Impairment, elderly, people with different physical and cognitive abilities. In this paper, we propose a method for extracting and analyzing physiological data by using contactless sensors (i.e., RGB and thermal cameras). The physiological parameters that can be analyzed are: the respiration rate, blinking rate, and the temperature variation across different regions on the face (i.e., the forehead, the nose, the peri-nasal region, the left and right periorbital regions, and the entire face). These parameters, together with the action units (AUs) can be indicators of the current internal state of an user. We analyze data from three different scenarios and report the results obtained.

In order to appear as natural as possible during social interaction, robots need either the ability to express or to measure emotions. Touch can be a very powerful communication modality but is very little exploited. Our paper aims to create a model of the tactile features activated during a handshake act, that can discriminate intrinsic characteristics of a person such as gender or extroversion. First, we construct a model of handshaking based on the human-human styles of handshaking. This model is thereafter compared with a human-robot handshaking interaction. The Meka robot is used in our experiments. A method is proposed to manage the modeling using feature selection with ANOVA and linear discriminant analysis (LDA). The first preliminary results show that it is possible to recognize gender and extroversion personality trait based on the firmness and movement of the style of handshaking. For instance, smaller pressure and frequency were found to describe female handshakes and higher speed amplitude describe introverted handshakes. Consistency was also found when comparing human-human handshaking with human-robot ones. These are encouraging results that will allow us to develop personalized interactions.

This paper describes a hands-off socially assistive therapist robot designed to monitor, assist, encourage, and socially interact with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion–introversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward customized post-stroke rehabilitation therapy based on the user’s personality traits and task performance. Three validation experiment sets are described. The first maps the user’s extroversion–introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing. The second and the third experiments adjust the personality matching dynamically to adapt the robot’s therapy styles based on user personality and performance. The reported results provide first evidence for user preference for personality matching in the assistive domain and demonstrate how the socially assistive robot’s autonomous behavior adaptation to the user’s personality can result in improved human task performance.

International audience; In Human-Robot Interaction (HRI) scenarios , an intelligent robot should be able to synthesize an appropriate behavior adapted to human profile (i.e., personality). Recent research studies discussed the effect of personality traits on human verbal and nonverbal behaviors. The dynamic characteristics of the generated gestures and postures during the nonverbal communication can differ according to personality traits, which similarly can influence the verbal content of human speech. This research tries to map human verbal behavior to a corresponding verbal and nonverbal combined robot behavior based on the extraversion-introversion personality dimension. We explore the human-robot personality matching aspect and the similarity attraction principle, in addition to the different effects of the adapted combined robot behavior expressed through speech and gestures, and the adapted speech-only robot behavior, on interaction. Experiments with the humanoid NAO robot are reported.

Socially intelligent robotics is the pursuit of creating robots capable of exhibiting natural-appearing social qualities. Beyond the basic capabilities of moving and acting autonomously, the field has focused on the use of the robot's physical embodiment to communicate and interact with users in a social and engaging manner. One of its components, socially assistive robotics, focuses on helping human users through social rather than physical interaction. Early results already demonstrate the promises of socially assistive robotics, a new interdisciplinary research area with large horizons of fascinating and much needed research. Even as socially assistive robotic technology is still in its early stages of development, the next decade promises systems that will be used in hospitals, schools, and homes in therapeutic programs that monitor, encourage, and assist their users. This is an important time in the development of the field, when the board technical community and the beneficiary populations must work together to shape the field toward its intended impact on improved human quality of life

For companion robots to properly interact with people, they should be able to gain awareness of their effect on the person's state and learn to behave accordingly. Advances in physiological monitoring sensors, such as infrared cameras, may enable robots to get an insight on a person's emotional state, which has a strong correlation with the physiological parameters. Performing this monitoring in a contact-free, user transparent manner is an important requirement. In this paper, we used a thermal imaging sensor mounted on the Meka humanoid robot, to remotely measure temperature variations on the face of the person while interacting with it. The scenario was a card-based quiz with the robot, which was aimed to induce a stressful state and 16 participants took part in the experiment. Several conditions with variations in the distance, height, and gaze of the robot were tested. A statistically significant variation in temperature in the nasal area of the face was measured while the robot was standing in the personal space of the participant. The first preliminary results encourage us to believe that temperature variations across various areas of people's faces can hint companion robots to adjust their behavior in human interactions.

A typical feature of robotic architectures are its reactivity and self-adaptivity. In practice, this is achieved by context-dependent dynamic invocation of software components in robotic architectures. In this paper, we specifically address how this self-adaptation capability can be formally defined and modeled in an architecture-independent way. We propose a probabilistic approach that facilitates system design and dynamic runtime adaptation satisfying the quality requirements. We also show how such techniques are incorporated in our model-driven framework: Self Adaptive Framework for Robotic Systems.

A common goal of robot control architecture designers is to create systems that are sufficiently generic to be adapted to different robot hardware. Beyond code re-use from a software engineering standpoint, having a common architecture could lead to long-term experiments spanning multiple robots and research groups. This paper presents a first step toward this goal with HBBA, a Hybrid Behavior-Based Architecture first developed on the IRL-1 humanoid robot and integrating an Adaptive Resonance Theory-based episodic memory (EM-ART). This paper presents the first step of the adaptation of this architecture to two different robots, a Meka M-1 and a NAO from Aldebaran, with a simple scenario involving learning and sharing objects' information between both robots. The experiment shows that episodes recorded as sequences of people and objects presented to one robot can be recalled in the future on either robot, enabling event anticipation and sharing of past experiences.

International audience; An intelligent robot needs to be able to understand human emotions, and to understand and generate actions through cognitive systems that operate in a similar way to human cognition. In this chapter, we mainly focus on developing an online incremental learning system of emotions using Takagi-Sugeno (TS) fuzzy model. Additionally, we present a general overview for understanding and generating multimodal actions from the cognitive point of view. The main objective of this system is to detect whether the observed emotion needs a new corresponding multi-modal action to be generated in case it constitutes a new emotion cluster not learnt before, or it can be attributed to one of the existing actions in memory in case it belongs to an existing cluster.

Robotics is moving towards real world applications, beyond the well-structured environment of industrial robotics. In the world of assistant robots and medical robots, Human-Robot Interaction is essential. Also in emerging industrial scenarios there is a need of the human to be closely included in the loop. The companies are confronted with the lack of guidelines and of standards on how the higher features of HRI may be safely incorporated. Although the scientific research is burgeoning and worthy of praise, it is clear that its results are scattered and not capable of giving a clear input to be easily taken up by companies and standardization organizations like ISO and IEC. The workshop aims at the integration of empirical findings into complex real world robot systems by focusing on three typical sectors (industrial, service and medical) to develop systematic approaches to benchmark and evaluate experimental systems so that normative results can be realized rapidly. The present workshop focuses on bringing together scientists, representative of robotics companies and of standardization working groups to foster discussion in the definition of experimental scenarios and protocols in HRI, so to be able to prime real world set-ups and help realize the robotic products of the future.

The recent trend toward developing a new generation of robots that are capable of moving and acting in human-centered environments, interacting with people, and participating in our daily lives has introduced the need for building robotic systems able to learn how to use their bodies to communicate and to react to their users in a social and engaging way. Social robots that interact with humans have thus become an important focus of robotics research.

International audience; In human-human interaction, the process of communication can be established through three modalities: verbal, non-verbal (i.e., gestures), and/or para-verbal (i.e., prosody). The linguistic literature shows that the para-verbal and non-verbal cues are naturally aligned and synchronized, however the natural mechanism of this synchronization is still unexplored. The difficulty encountered during the coordination between prosody and metaphoric head-arm gestures concerns the conveyed meaning , the way of performing gestures with respect to prosodic characteristics, their relative temporal arrangement, and their coordinated organization in the phrasal structure of utterance. In this research, we focus on the mechanism of mapping between head-arm gestures and speech prosodic characteristics in order to generate an adaptive robot behavior to the interacting human's emotional state. Prosody patterns and the motion curves of head-arm gestures are aligned separately into parallel Hidden Markov Models (HMM). The mapping between speech and head-arm gestures is based on the Coupled Hidden Markov Models (CHMM), which could be seen as a multi-stream collection of HMM, characterizing the segmented prosody and head-arm gestures' data. An emotional state based audio-video database has been created for the validation of this study. The obtained results show the effectiveness of the proposed methodology.

International audience; A good adjustable control system is a system that can adapt its behaviors according to the user's state over time. While most researches in Human-Robot Cooperation focus on making the robotic system more efficient and effective in task accomplishment, we argue that very few works tried to model the human operator. In this paper, we discuss several aspects related to the human operator that we consider important for an efficient Human-Robot Cooperation. Furthermore, we present a tentative architecture of an adjustable control system, where these aspects are emphasized.

International audience; In human-robot interaction, a social intelligent robot should be capable of understanding the emotional internal state of the interacting human so as to behave in a proper manner. The main problem towards this approach is that human internal states can't be totally trained on, so the robot should be able to learn and classify emotional states online. This research paper focuses on developing a novel online incremental learning of human emotional states using Takagi-Sugeno (TS) fuzzy model. When new data is present, a decisive criterion decides if the new elements constitute a new cluster or if they confirm one of the previously existing clusters. If the new data is attributed to an existing cluster, the evolving fuzzy rules of the TS model may be updated whether by adding a new rule or by modifying existing rules according to the descriptive potential of the new data elements with respect to the entire existing cluster centers. However, if a new cluster is formed, a corresponding new TS fuzzy model is created and then updated when new data elements get attributed to it. The subtractive clustering algorithm is used to calculate the cluster centers that present the rules of the TS models. Experimental results show the effectiveness of the proposed method.

International audience; We present a mobile robot whose goal is to autonomously explore an unknown indoor environment and to build a semantic map containing high-level information similar to those extracted by humans. This information includes the rooms, their connectivity, the objects they contain and the material of the walls and ground. This robot was developed in order to participate in a French exploration and mapping contest called CAROTTE whose goal is to produce easily interpretable maps of an unknown environment. In particular we present our object detection approach based on a color+depth camera that fuse 3D, color and texture information through a neural network for robust object recognition. We also present the material recognition approach based on machine learning applied to vision. We demonstrate the performances of these modules on image databases and provide examples on the full system working in real environments.

International audience; The human perception of the external world appears as a natural, immediate and effortless task. It is achieved through a number of "low-level" sensory-motor processes that provide a high-level representation adapted to complex reasoning and decision. Compared to these representations, mobile robots usually provide only low-level obstacle maps that lack such high- level information. We present a mobile robot whose goal is to autonomously explore an unknown indoor environment and to build a semantic map containing high-level information similar to those extracted by humans and that will be rapidly and easily interpreted by users to assess the situation. This robot was developed under the1 Panoramic and Active Camera for Object Mapping (PACOM) project whose goal is to participate2 in a French exploration and mapping contest called CAROTTE . We will detail in particular how we integrated visual object recognition, room detection, semantic mapping, and exploration. We demonstrate the performances of our system in an indoor environment.

In human-robot interaction, gender and internal state detection play an important role in making the robot reacting in an appropriate manner. This research focuses on the important features to extract from a voice signal in order to construct successful gender and internal state detection systems, and shows the benefits of combining both systems together on the total average recognition score. Moreover, it consists a foundation on an ongoing approach to estimate the human internal state online via unsupervised clustering algorithms.

In this paper, we present the first steps towards a new concept of robot self-awareness that can be implemented into embodied robot systems. Our concept of "the self" is inspired by already existing approaches and aims to provide a cognitive system with meta-cognitive capabilities. We believe that robot self-awareness is a crucial factor in the improvement of HRI.

This paper addresses gait analysis for a mobility assistance robot designed for the elderly people. Six patients and ten healthy peoples were invited to be part of our first pilot experiment. We designed two experiments so as to firstly detect gait parameters and secondly to identify a change of speed. For the first trial, we compared the temporal-distance parameters of the healthy people and of individuals suffering of mobility problems. The percentages of the gait cycle for duration of stance are higher for people with mobility impairment than for healthy people. In the second experiment, we detected the change in walking speed from the ten healthy peoples. Two different metrics derived from the Kullback-Leibler (KL) divergence and from the Generalized Likelihood Ratio (GLR) were employed for walking change detection. The Receiver Operating Characteristic (ROC) curves show a better performance for the signal obtained with the accelerometer sensor than that obtained with the infrared distance sensor. Nevertheless, the results of our experiments demonstrated that both methodologies (KL and GLR) can be used to detect the change points during walking at high or slow speed.

Currently the 2 percent growth rate for the world's older population exceeds the 1.2 rate for the world's population as a whole. By 2050, the number of individuals over the age 85 is projected to be three times more than there is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new adaptive robotic system based on the socially assistive robotics (SAR) technology that tries to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and/or Alzheimer's disease. Our results show that this approach can engage the patients and keep them interested in interacting with the robot, which, in turn, increases their positive behavior.

Currently the 2 percent growth rate for the world's older population exceeds the 1.2 percent rate for the world's population as a whole. This difference is expected to increase rather than diminish so that by 2050, the number of individuals over the age 85 is projected to be three times what it is today. Most of these individuals will need physical, emotional, and cognitive assistance. In this paper, we present a new system based on the socially assistive robotics (SAR) technology that will play the role of a music therapist and will try to provide a customized help protocol through motivation, encouragements, and companionship to users suffering from cognitive changes related to aging and/or Alzheimer's disease.

This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterize a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modeling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are efectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view eld of 45 º. In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization., This work was partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, and the MID-CBR project grant TIN2006-15140- C03-01 and FEDER funds and the grant 2005-SGR-00093 and the MIPRCV Consolider Imagennio 2010.

IEEE International Conference on Robotics and Automation (ICRA 2008, Pasadena, California, May 19-23, 2008), pp. 538-543., This paper presents a vision-based approach for mobile robot localization. The environmental model is topological. The new approach uses a constellation of different types of affine covariant regions to characterize a place. This type of representation permits a reliable and distinctive environment modeling. The performance of the proposed approach is evaluated using a database of panoramic images from different rooms. Additionally, we compare different combinations of complementary feature region detectors to find the one that achieves the best results. Our experimental results show promising results for this new localization method. Additionally, similarly to what happens with single detectors, different combinations exhibit different strengths and weaknesses depending on the situation, suggesting that a context-aware method to combine the different detectors would improve the localization results., This work was partially supported by USC Women in Science and Engineering (WiSE), the FI grant from the Generalitat de Catalunya, the European Social Fund, and the MID-CBR project grant TIN2006-15140-C03-01 and FEDER funds and the grant 2005-SGR-00093.

Environment representation and situation awareness are key issue for personal and service robotics. Recent research in mobile robotics mainly concentrated on environment representations for efficient and robust navigation (e.g. Simultaneous Localization and Mapping). However, personal robots need competences that go beyond navigation and thus environment representations that include relationships and semantics of objects. Only this will enable personal robots to reason about the environment and develop cognitive competencies.

Proceedings. 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), October 27-31, 2003, Las Vegas, Nevada, ISBN:0-7803-7861-X, ISBN:0-7803-7860-1

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported.

In visual-based robot navigation, panoramic vision emerges as a very attractive candidate for solving the localization task. Unfortunately, current systems rely on specific feature selection processes that do not cover the requirements of general purpose robots. In order to fulfill new requirements of robot versatility and robustness to environmental changes, we propose in this paper to perform the feature selection of a panoramic vision system by means of the saliency-based model of visual attention, a model known for its universality. The first part of the paper describes a localization system combining panoramic vision and visual attention. The second part presents a series of indoor localization experiments using panoramic vision and attention guided feature detection. The results show the feasibility of the approach and illustrate some of its capabilities.

Even today, robot mapping is one of the biggest challenges in mobile robotics. Geometric or topological maps can be used by a robot to navigate in the environment. Automatic creation of such maps is still problematic if the robot tries to map large environments. This paper presents a new method for incremental mapping using fingerprints of places. This type of representation permits a reliable, compact, and distinctive environment-modeling and makes navigation and localization easier for the robot. Experimental results for incremental mapping using a mobile robot equipped with a multi-sensor system composed of two 180/spl deg/ laser range finders and an omni-directional camera are also reported.

This paper addresses the problem of perception and representation of space for a mobile agent. A probabilistic hierarchical framework is suggested as a solution to this problem. The method proposed is a combination of probabilistic belief with “Object Graph Models” (OGM). The world is viewed from a topological optic, in terms of objects and relationships between them. The hierarchical representation that we propose permits an efficient and reliable modeling of the information that the mobile agent would perceive from its environment. The integration of both navigational and interactional capabilities through efficient representation is also addressed. Experiments on a set of images taken from the real world that validate the approach are reported. This framework draws on the general understanding of human cognition and perception and contributes towards the overall efforts to build cognitive robot companions.

In this paper a hierarchical multi-resolution approach allowing for high precision and distinctiveness is presented. The method combines topological and metric paradigm. The metric approach, based on the Kalman Filter, uses a new concept to avoid the problem of the drift in odometry. For the topological framework the fingerprint sequence approach is used. During the construction of the topological map, a communication between the two paradigms is established. The fingerprint used for topological navigation enables also the re-initialization of the metric localization. The experimentation section will validate the multi-resolution-representation maps approach and presents different steps of the method.

Welcome to the Intelligent Service Robotics special issue on Multidisciplinary Collaboration on Socially Assistive Robotics. This special issue tries to address a powerful new trend in robotics research, that of robots getting closer to humans and the need to endow them with the abilities of interaction so that they can be productively integrated into our lives. Human–robot interaction (HRI) for socially assistive applications is still in its infancy, but it is a growing and increasingly popular research area at the intersection of robotics, health science, psychology, social and cognitive sciences. Assistive robotics has the potential to enhance the quality of life for large populations of users. In response to the rapidly growing elderly population, research attention has been turned to the study and development of robot pets, companions and cognitive and mobility aids aimed at reducing stress and depression and maintaining autonomy. In addition, individuals with physical impairments and those in rehabilitation therapy are also potential beneficiaries of socially assistive technologies, not only for improved mobility but also for improved outcomes in recovery. Finally, individuals with cognitive disabilities and developmental and social disorders (e.g., autism) constitute another growing population that could benefit from assistive robotics in the context of special education, therapy and training. An effective socially assistive robotmust understand and interact with its environment, exhibit social behavior and focus its attention and communication on the user to help her/him achieve goals.

Welcome to the Autonomous Robots special issue on the emerging area of Socially Assistive Robotics (SAR). What is SAR? Socially assistive robotics focuses on providing assistance through social rather than physical interaction between the robot and the human user. SAR has the potential to enhance human quality of life for large user populations, including the elderly, people with physical impairments, those involved in rehabilitation therapy (e.g., post-stroke patients), and people with cognitive disabilities and social and developmental disorders (e.g., children with autism spectrum disorder, children with attention deficit/hyperactivity disorder (AD/HD)). Socially assistive robotics has a great variety of potential uses and associated multi-faceted research and usability challenges. Numerous interesting questions and issues need to be addressed as we aim toward including robust, reliable, user-friendly, empathetic, and encouraging robots in our daily lives. Those questions include: “What are the circumstances in which people (especially those with special needs) accept an assistive robot in their environment?” “What modes of communication should the robot employ?” “What is the role of the robot’s physical embodiment?” “How to integrate the a priori knowledge about the users into the robot system?” “How can the human-robot interaction design ensure safety?” and “How are friendly, familiar, usable, and effective interaction models with a robot to be designed?”

In this work we address the problem of perception, spatial cognition and topological navigation for a mobile robot. The objective of this work is to enable the navigation of an autonomous mobile robot (or vehicle) in an indoor (or outdoor) structured environment without relying on maps a priori learned and without using artificial landmarks. A new method for incremental and automatic topological mapping and global localization using fingerprints of places is presented. The fingerprint-based representation permits a reliable, compact and distinctive environment-modeling. Experimental results for mapping indoor and outdoor environments with a mobile robot and a "SMART" vehicle, both equipped with a multi-sensor system composed of two 180deg laser range finders and an omnidirectional camera are also reported

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/BSBTCD04/ address: Dagstuhl (DE) editor: Iida, F. and Pfeifer, R. and Steels, L. and Kuniyoshi, Y. publisher: Springer-Verlag; Cognition and Reasoning with uncertain and partial knowledge is prob- ably the biggest challenge for autonomous mobile robotics. Previous robotics sys- tems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a ma jor challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and method- ology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. This original formalism will be exemplified by two interesting experiments where robots are driven by a Bayesian Program (BP). These examples represents situations where the robot can sense only a small part of its global environment using noisy sensors. The second fact about these environments is they cannot be constrained so that to ease the control of the robot.