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Purpose Many work conditions require manipulators to open cabinet doors and then gain access to the desired workspace. However, after opening, the unlocked doors can easily close, interrupt a task and potentially break the operating end-effectors. This paper aims to address a manipulator's behavior planning problem for responding to a dynamic workspace released by door opening. Design/methodology/approach A dynamic model of the restricted workspace released by an unlocked door is established. As a whole system to treat, the interactions between the workspace and robot are analyzed by using a partially observable Markov decision process. A self-protective policy decision executed as a belief tree is proposed. To respond to the policy, this study has designed three types of actions: stay on guard in the workspace, using an elbow joint to defense the door and linear escape out of the workspace for self-protection by observing collision risk levels to trigger them. Finally, this study proposes self-protective motion controllers based on risk time optimization to act to the planned actions. Findings The elbow defense could balance robotic safety and work efficiency by interrupting the end-effector's work and using the elbow joint to prevent the door-closing in an active collision way. Compared with the stay and escape action, the advantage of the elbow defense is having a predictable performance to quick callback the interrupted work after the risk was relieved. Originality/value This work provides guidance for the safe operation of a class of robot operations and the upgrade of motion planning.

Telemanipulation in power stations commonly require robots first to open doors and then gain access to a new workspace. However, the opened doors can easily close by disturbances, interrupt the operations, and potentially lead to collision damages. Although existing telemanipulation is a highly efficient master–slave work pattern due to human-in-the-loop control, it is not trivial for a user to specify the optimal measures to guarantee safety. This paper investigates the safety-critical motion planning and control problem to balance robotic safety against manipulation performance during work emergencies. Based on a dynamic workspace released by door-closing, the interactions between the workspace and robot are analyzed using a partially observable Markov decision process, thereby making the balance mechanism executed as belief tree planning. To act the planning, apart from telemanipulation actions, we clarify other three safety-guaranteed actions: on guard, defense and escape for self-protection by estimating collision risk levels to trigger them. Besides, our experiments show that the proposed method is capable of determining multiple solutions for balancing robotic safety and work efficiency during telemanipulation tasks.

The articles in this special section focuses on the use of socially assistive robots for use in the healthcare industry. Due to the serious shortage of healthcare professionals, socially assistive robots are proving themselves a viable complementary tool to address this demand. Motivated by these concerns and the benefits of using robotic systems to provide customized help, the goal of this special issue is to provide a snapshot of state-of-the-art advancements and recent novel results in the field of socially assistive robotics.

For more socially natural interactions between robots and humans there is a need for the robots to detect and understand the current internal state of the individuals they interact with. In this paper, we investigate the impact of personality, user sensory profile, and morningness–eveningness type of an individual on the variation of different physiological parameters (i.e., blinking, galvanic skin response, facial temperature variation), as well as the performance in a non-verbal word-color Stroop task. The TIAGo humanoid robot displayed two interaction styles that are based on visual and auditory stimuli. The visual and auditory stimuli used by the robot are in accordance with the user sensory profile. We report the results of a within participants study carried out with 24 participants. Our results show that personality, user sensory profile, and morningness–eveningness type have an impact on the variation of the chosen physiological parameters. Furthermore, the interaction style of the robot has an influence on the variation of the physiological parameters.

Many Advanced Driver Assistance Systems (ADAS) and chassis systems can be found within the same passenger car. Most of these systems are controlled separately using standalone controllers. Several studies show the benefits of coordinating several systems especially in simultaneous operations. However, most of the studies tend to ignore the time-delays that exist in the control loop. This time-delays can destabilize the vehicle in severe situations. This paper provides a solution to face time-delays in the context of integrated ADAS and chassis systems. Results show the benefits of the control logic proposed in terms of stability and performance. This brings one step closer towards standardization of global vehicle motion control.

International audience; Most of recent researches on the automotive field focus on autonomous vehicles. These vehicles are equipped with conventional chassis systems. The goal is to control the vehicle's traction, brakes, and front steering. This paper discusses the importance of advanced chassis systems, as driving/braking torque vectoring, for both autonomous and non-autonomous vehicles, especially in a race mode. Reliable co-simulation results show that expanding the vehicle's potential leads to high performances and safety with respect to severe situations when optimal control allocation is ensured. Therefore, future passenger cars shall not only be equipped with additional sensors, but also by advanced systems along with adequate control algorithms.

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; As we are heading towards autonomous vehicles, additional driver assistance systems are being added. The vehicle motion is automated step by step to ensure passengers' safety and comfort, while still preserving vehicle performance. However, simultaneous activations of concurrent systems may conflict, and non-suitable behavior may emerge. Our research work consists in proving that with the right coordination approach, simultaneous operation of different systems improve the vehicle's performance and avoid the emergence of unwanted conflicts. To prove this, we gathered different control architectures implemented in commercial passenger cars, and we compared them with our control architecture using a unified reference vehicle model. The high-fidelity vehicle model is developed in Simcenter Amesim in a modular and extensible manner. This enables adding systems in a plug-and-play way. Not only different control architectures can be tested on the same vehicle, but also different systems combinations can be evaluated. In this research, the vehicle can steer at the front and at the rear, and each wheel can be braked independently. Each of the actuators concerned can influence the vehicle's yaw rate leading in some cases in system conflicts. More complex control strategies are then implemented in Matlab/Simulink, and co-simulations are carried between both softwares in order to provide realistic results. It has been shown that optimal control allocation algorithms are more suitable to coordinate systems in an over-actuated vehicle. Moreover, if the optimization objectives are well formalized, performance, safety and comfort can be improved since the vehicle can benefit from the systems' synergies.

Trust is related to the performance of human teams, making it a significant characteristic, which needs to be analyzed inside human-robot teams. Trust was researched for a long time in other domains like social sciences, psychology, and economics. Building trust within a team is formed through common tasks and it depends on team performance and communication. By applying an online game based tasks for human-robot teams, the effects of three communication conditions (communication without text and verbal interaction, communication with text and verbal interaction related/not related to the task) on trust are analyzed. Additionally, we found that the participants’ background is linked to the trust in the interaction with the robot. The results show that in a human-robot team the human trust will increase more over time when he/she is working with a robot that uses text and verbal interaction communication related to the task. They further suggest that human trust will decrease to a lower extent when the robot fails in doing the tasks if it uses text and verbal communication with the human.

International audience; Several systems are integrated in passenger cars. Some of them are just redundant systems due to safety requirements. Others, are completely different and can interact with each other as long as they are operating inside the same vehicle. Control allocation methods have been successfully implemented in advanced aircrafts to avoid conflicts, especially in the context of redundant systems. In this paper, we will rather focus on coordinating non-redundant advanced chassis systems with different dynamics. This difference in dynamics can be especially problematic when systems exhibit different communication delays. Model Predictive Control Allocation (MPCA) methods are therefore investigated in order to activate the right system at the right moment. Results show that particularly when the most effective system is saturated, another system with a different time delay can be activated few steps before saturation to instantly take over the maneuver. With good knowledge of actuator dynamics and higher computation power, MPCA methods are able to solve complex problems in severe situations.

International audience; Many car manufacturers are equipping their vehicles with advanced systems to win the race towards autonomous vehicles. In order to ensure multiple objectives, different embedded systems influencing differently the same physical variable are often implemented in the same vehicle. Most of car manufacturers tend to activate one system at a time, especially when the low-level control of actuators is developed by different equipment suppliers, making the inner dynamics uncertain. However, this limits the potential of the vehicle. This paper discusses the feasibility of coordinating these systems when activated at the same time, while ensuring an acceptable robustness regarding the uncertain dynamics. Results showed good performance in severe maneuvers when combining robust control synthesis and optimization-based control allocation algorithms. Therefore, several embedded systems may be activated at the same time to expand the overall potential of the vehicle and deal with more difficult situations.

International audience; Most of today's passenger cars are over-actuated. This over-actuation is expected to grow going towards self-driving cars. However, the necessary systems that would be needed for a safe and a comfortable autonomous driving remain uncertain. This paper aims to provide an extensible control architecture that does not depend on a particular system combination. Car manufacturers tend to develop ad hoc rule-based strategies for a specific set of integrated systems. Through several realistic scenarios, we have shown that the control architecture proposed in this paper can be adopted for different set of integrated systems. A minimal effort is required from the control designer when changing the nature of the systems integrated to adapt the control strategy. The control architecture proposed in this paper can serve as a starting point for the standardization of control architectures for future vehicles motion control.

Several modern vehicles provide the option to select a driving mode. However, the literature contains no empirical studies that investigate how driving modes affect the vehicle's dynamic behaviour in regular on-road driving. We examined for which CAN-bus signals the differences between Renault's Multi-Sense® comfort and sport modes are most apparent. We gathered data on a 26.3 km route containing a rural and highway section. A single person drove the route four times in comfort mode and four times in sport mode. By statistically analysing and ordering 887 CAN-bus signals, we found strong differences between the two modes for rear-wheel angle, engine torque, longitudinal acceleration, and vertical motion. Parameter identification of a quarter car model identified a 3.5 times higher damping coefficient for the sport mode compared to the comfort mode. Due to four wheel steering, compared to the comfort mode, the sport mode yielded a higher lateral acceleration and yaw rate for a given steering wheel angle and driving speed. In conclusion, this study provides quantitative insight into the extent to which the Multi-Sense driving modes impact the vehicle's lateral, longitudinal, and vertical dynamic behaviour. The results and the analysis methods help guide future driving mode designs.

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.

International audience; Autonomous vehicles are round the corner. Most of car manufacturers are racing to become a leader in this promising market. In this paper, we focus on one of the remaining challenges to be overcome, namely, the control of vehicle motion when friction varies. Researchers tend to estimate the friction coefficient using complex observers based on simple mathematical equation. We believe that by taken into account some formerly simplified dynamics, as roll dynamics, we can reduce the estimations' errors, and therefore use simple observers. Co-simulations of the control logic with a high-fidelity vehicle model proved the relevance of this method. Results showed that the vehicle can be controlled in severe conditions while the friction changes using only today's passenger cars sensors. Vertical dynamics should therefore be taken into account even for plane motions.

International audience; Vehicle motion control has many challenges to overcome. One of the main problems is robustness against not only environmental changes but also uncertainties about the vehicle itself. This paper focuses on this problem using robust control design at the control architecture's high level. Researches tend to decentralize the control to treat longitudinal and lateral dynamics separately. Here, an overall vehicle model is first proposed and studied to justify the structure that the high-level controller should embrace. Co-simulation results of different combinations showed promising performances to face uncertainties and couplings. Therefore, robust techniques combined with control allocation techniques may enhance autonomous vehicles reliability.

International audience; This paper investigates how individuals react in a situation when an experimenter (human or robot) either tells them to stop in the middle of playing the Jenga game, or accidentally bumps into a table and makes the tower fall down. The mood of the participants and different physiological parameters (i.e., galvanic skin response (GSR) and facial temperature variation) are extracted and analysed based on the condition, experimenter, and psychological questionnaires (i.e., TEQ, TEIQ, RST-PQ). This study was a between participants study with 23 participants. Our results show that multiple GSR parameters (e.g., latency, amplitude, number of peaks) differ significantly based on the condition and the experimenter the participants interacted with. The temperature variation in three regions of interest (i.e., forehead, left, and right periorbital regions) are good indicators of how ready an individual is to react in an unforeseen situation.

International audience; — Enabling robots to determine how physiological parameters vary in relation to the profile of an individual can lead to a better adaptation of the behavior of the robot to the needs of the individual it interacts with. This paper investigates how physiological parameters (i.e., blinking, galvanic skin response (GSR), facial temperature variation) vary in a news reading task, based on various types of stimuli (i.e., auditory and visual) that TIAGo robot uses to present the news. The results from a within participant study with 11 participants are reported. We also consider the impact of personality and the user sensory profile (based on Adult and Adolescent Sensory Profile (AASP)) for the physiological parameters variation. Our results show that blinking is the main physiological parameter that varies in the non-stressing task of news reading.

Social robots are expected to be part of everyday life of people. This will generate interactions between humans and robots that may have positive or negative effects on the users. In order to minimize the negative effects and increase robot persuasiveness, robots should behave in an appropriate manner by adapting to their users. How to achieve this adaptation remains a challenge. We propose the usage of the Regulatory Focus Theory, user physiological state, and game-performance information in order to detect user stress and adapt the behavior of the robot. We present a longitudinal experiment conducted with 35 participants in a game-like scenario. The robot was trained for adapting to the regulatory focus of the users and decreasing their stress while they were playing the game. For this reason, we trained the robot with 12 participants with Chronic Promotion State and with 12 participants with Chronic Prevention State. We used a Q-Learning algorithm based on the Regulatory Focus of the participants, user stress, and task performance. The model obtained was tested with 2 groups (6 and 5 participants, respectively) according to their Chronic Regulatory Focus. Results show that our system was able to generate a robot behavior capable of increasing robot persuasiveness and reducing user stress, which is of great importance for social robots.

International audience; Redundancy of integrated systems has always been used in order to increase vehicle's safety. Some new technologies are however too expensive to be redundant. This paper proposes a new way to preserve the safety by enabling a kind of complementarity between different chassis systems. Optimization-based control allocation redistribution algorithms are used in order to find the best way to combine conflicted systems. Results have shown that one system can take over another one when a failure occurs making the control logic fault-tolerant and reconfigurable. This suits better future cars as additional chassis systems are intended to be integrated.

International audience; — In the context of a worldwide aging population, it is important to find solutions to help the elderly maintain their cognitive functions. This research was done in the context of the ENRICHME. We investigate the outcome of a 5 day intensive cognitive stimulation with an elderly individual. Each day was composed of two sessions (one at 11am, and one at 3pm). During each session the participant played three cognitive games (i.e., digit cancellation, integer matrix task, Stroop game), two of them having two difficulty levels. The mood of the participant was also recorded before and after each interaction session. Evidence was found that even after a few sessions, the performance of the participant increased for all games. The performance of each game and each difficulty level was analyzed based on the interaction time (11am or 3pm) and the interaction day. A detailed analysis of the performance is presented together with a discussion of these results.

Companion robots are more and more taking the role of caregivers for elderly people. Elderly people sometimes take the advice given by their family members or caregivers as a criticism. In this context, persuasive communication skills could be helpful. A social psychology theory called Regulatory Focus states that people have one of two inclinations when taking decisions: Promotion or Prevention Focus. Also, based on these inclinations, people can be influenced by the way the message is sent, including the speed of the speech and the amplitude of body gestures. In this paper, we analyze the influence of Regulatory Focus on a negotiation scenario, using 3 conditions: (1) a robot with a promotion behavior, (2) a robot with a prevention behavior, and (3) a robot with a neutral behavior. Our results support the results found in the psychology literature related to Regulatory Focus, suggesting that Promotion participants were more influenced by the robot showing a Promotion based behavior. Moreover, Prevention participants were more relaxed on the condition with the robot showing a Prevention based behavior, and accepted the biggest concession between the initial and final offer.

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; Most of automotive researches focus on autonomous vehicles. Studies regarding trajectory planning and trajectory tracking became preponderant. As in case of commercial ground vehicles there is a driver in the loop, one should raise the important question of how the trajectory should be tracked. In this paper, we investigate the influence of controlling integrated chassis systems on the vehicle's behavior. A fixed Model Predictive Control is used to track the trajectory. Tunable vehicle motion control is however used to provide different motion feelings. Results show that a specific trajectory could be followed in different manners. Therefore, vehicle dynamics can be and should be controlled in such a way to generate adaptive trust feelings to passengers in case of autonomous driving.

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.

International audience; Most of the chassis systems are developed by automotive suppliers to improve a specific vehicle performance. Drivers, and consequently vehicle manufacturers, are more concerned by the overall behaviour of the Vehicle. Many coordination architectures have been proposed in the literature in order to integrate different chassis systems in a single vehicle. In this paper , these architectures are compared and discussed. Two major classes are proposed: Downstream and Upstream Coordination. The purpose of this classification is to help car manufacturers and suppliers standardize an Integrated Vehicle Dynamics Control architecture for faster and more flexible designs.

International audience; —A large interest has recently been given to global chassis control. This requires a tire model adapted to control synthesis that can represent combined slip. In this paper, a new suitable tire model for vehicle coupled operations control is proposed. The existing models are either too simple to represent coupled behaviors or too complex to allow control synthesis. This new tire model takes benefits from both approaches, as it is linear with varying parameters. Simulations of coupled operations proved the model accuracy in the tire stable range and its promising potential for global chassis control synthesis.

For service robots to be well received in our daily lives, it is desirable that they appear as friendly as possible rather than some unfriendly characters. While a robot's physical appearance influences this perception, its behavior also has an impact. In order to be sure that a specific robot behavior will be correctly perceived, we propose to its potential users to shape the robot's behavior. In this paper, a specific behavior, “approaching a person”, is evaluated with a Robosoft Kompai robot. To avoid logistics issues associated with having large groups of novice users performing demonstrations on a physical robot, a web-based approach built around a simulation of the actual robot is proposed. The relationship between the robot and the person is described by the two dimensions of the interpersonal circumplex: communion (hostile or friendly) and agency (submissive or dominant). The users can adjust three parameters of the approach behavior (i.e., distance, trajectory curvature, and deceleration) in a manner that corresponds the best to the described relationship. An analysis of the data from 69 users is presented, along with a verification experiment done with 10 participants and the real robot. Results suggest that users associate hostile robots with straight trajectories, and submissive robots with smoother deceleration.

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".

Many challenges still need to be overcome in the context of autonomous vehicles. These vehicles would be over-actuated and are expected to perform coupled maneuvers. In this paper, we first discuss the development of a global coupled vehicle model, and then we outline the control strategy that we believe should be applied in the context of over-actuated vehicles. A gain-scheduled H &infin, controller and an optimization-based Control Allocation algorithms are proposed. High-fidelity co-simulation results show the efficiency of the proposed control logic and the new possibilities that could offer. We expect that both car manufacturers and equipment suppliers would join forces to develop and standardize the proposed control architecture for future passenger cars.

International audience

International audience; The EU H2020 ENRICHME project is aimed at developing a socially assistive robot that can adapt its behavior based on the profile of the individual it interacts with. In this paper, we investigate how the ENRICHME project web-based News Application can be adapted based on the sensory profile and personality of the individual. The interaction between the robot and the individual takes place at three different distances (70cm, 1.2m, and 2m). The robot can use visual (showing or not showing the news on its touchscreen) and auditory (reading the news out loud) stimuli during the interaction. We looked at different physiological parameters (blinking, heart rate, respiration rate, and GSR) and we can report that we found statistical results that show that the physiological parameters vary based on interaction distance, condition, sensory profile, and personality.

For a greater acceptance of robots in home-like environments, there is a great need to adapt the behaviors of the robots to the profiles of the individuals they interact with. In this paper, we investigate the impact of the sensory profile of an individual over the robot styles of interaction displayed by a humanoid robot in a non-verbal word-color Stroop task. The style of interaction is based on different visual and auditory stimuli. The results of a within participants study carried out with 30 participants are reported. Our study suggests that the sensory profile of an individual should be taken into consideration when deciding the style of interaction between a robot and the person. We found statistical results that suggest that there is a difference in performance based on having either a low or high auditory category type.

International audience; The main purpose of the ENRICHME European project is to develop a socially assistive robot that can help the elderly, adapt to their needs, and has a natural behavior. In this paper, we present some of the lessons learnt from the first interaction between the robot and two elderly people from one partner care facility (LACE Housing Ltd, UK). The robot interacted with the two participants for almost one hour. A tremendous amount of sensory data was recorded from the multi-sensory system (i.e., audio data, RGB-D data, thermal images, and the data from the skeleton tracker) for better understanding the interaction, the needs, the reactions of the users, and the context. This data was processed offline. Before the interaction between the two elderly residents and the robot, a demo was shown to all the residents of the facility. The reactions of the residents were positive and they found the robot useful. The first lessons learnt from this interaction between Kompa¨ıKompa¨ı robot and the elderly are reported.

In Human-Robot Interaction (HRI), an important quantity of work has been done to investigate the reaction of people toward expressive robots. However, the large variability of available expression modalities (e.g., gaze, gestures, speech modulation) can make comparison between results difficult. We believe that developing a common taxonomy to describe these modalities would contribute to the standardization of HRI experiments. This paper proposes the first version of a classification system based on an analysis of humanoid robots commonly seen and used in HRI studies. Features from the face of robots are discussed in terms of functional and non-functional dimensions, and a short-hand notation is developed to describe these features.

One of the main purposes of companion robots is to use them to remind their users about the tasks they have to do. The interaction requires robots to adapt to the person with respect to their preferences. The performance of the human when a robot reminds them to do a certain task is of great importance. Findings in social psychology show that personality influences the way that humans interact. In this work, we conducted an experiment of task reminders in an office-like environment with a robot reminding tasks to a person while the person is doing other office-activities, with the goal of searching for positive influences of the robot on user’s personality. Nine different conditions were studied with the robot varying its behavior and appearance. Results show that the user’s personality has an influence on his/her time to perform a task while being reminded by a robot to perform such task, showing that people with high conscientiousness are more promoted by the robot to finish the task earlier than people with low conscientiousness, and also that introverted people are more motivated by the robot to finish the task earlier than extroverted people.

This paper presents a series of 4 single subject experiments aimed to investigate whether children with autism show more social engagement when interacting with the Nao robot, compared to a human partner in a motor imitation task. The Nao robot imitates gross arm movements of the child in real-time. Different behavioral criteria (i.e. eye gaze, gaze shifting, free initiations and prompted initiations of arm movements, and smile/laughter) were analyzed based on the video data of the interaction. The results are mixed and suggest a high variability in reactions to the Nao robot. The results are as follows: For Child2 and Child3, the results indicate no effect of the Nao robot in any of the target variables. Child1 and Child4 showed more eye gaze and smile/laughter in the interaction with the Nao robot compared to the human partner and Child1 showed a higher frequency of motor initiations in the interaction with the Nao robot compared to the baselines, but not with respect to the human-interaction. The robot proved to be a better facilitator of shared attention only for Child1. Keywords: human-robot interaction; assistive robotics; autism

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.

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; Robots are more and more present in our daily lives. In human- robot interaction, a social intelligent robot should be capable of understanding the context of interaction with the human so as to be- have in a proper manner by following some social rules. This paper focuses on the haptic affective social interaction during a greeting handshaking between the human and the robot. The main goal of this work is to study how the haptic feedback involved during the human-robot handshake can convey emotions, and more precisely, how it can influence the perception of emotions expressed through the facial expressions of the robot. Moreover, we examine the bene- fits of the multimodality (i.e., visuo-haptic) over the monomodality (i.e., visual-only and haptic-only). The experimental results with Meka robot show that the multimodal (i.e., visuo-haptic) condition presenting high values for grasping force and stiffness of move- ment are evaluated with higher values for the arousal and domi- nance dimensions than during the visual condition. Furthermore, the analysis of the results corresponding to the monomodal haptic condition showed that participants discriminate well the dominance and the arousal dimensions of the haptic behaviours presenting low and high values for grasping force and stiffness of movement.

In recent times, researchers in robotics have arrived at a consensus that there is no single efficient architecture or framework that satisfies all aspects of robotic system design. A majority of robotic architectures currently in use are developed in-house to meet the specific objectives of the research group. However, these frameworks have been designed in an ad-hoc manner and thus restricts reusability and scalability. In this paper, we propose an architecture description language ‘Architecture Modeling and Analysis Language (AMAL)’ that homogenizes the framework development process in a formal way. AMAL is a radical approach that enables framework development based on custom requirements or by integrating existing heterogeneous architectural paradigms. The Open Semantics Framework in AMAL facilitates domain knowledge integration and promotes separation of concerns to build complex systems. We also position our approach in the SafeRobots framework, a model-driven software development tool-chain for robotics.

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.

A robotic system is an integration of various systems such as perception, navigation, planner, controller, etc. The adaptation of the robotic system to the dynamic environments is embedded in the functionality of the constituent systems. This severely limits the configuration space of such systems. The modeling of the variability in the solution space can expand this design space, help finding the best possible solution, and perform run-time adaptation of the system. In this paper, a formal specification using primitive compositional elements is proposed to analyze the solution space and to streamline the decision making using non-functional properties of the system. The relevancy of the model is demonstrated using a case study on a vehicle tracking problem.

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.

In this paper, we present a new social intelligent robotic system used for cognitive stimulation therapy for individuals suffering from Mild Cognitive Impairment (MCI) and/or Alzheimer’s disease. Our work aims increasing the cognitive attention of users by playing different games and therefore help slow down cognitive decline. The system records the users’ task performance during the games and adapts the different levels of difficulty as a function of the users’ game history. The games were tailored to the needs of each individual so as to address their different cognitive disabilities. Two studies are presented.

International audience; Several decision making algorithms that are developed in robotics domain are found to be useful in automotive industrial applications. In order to use these algorithms in safety critical embedded systems one has to ensure a minimum confidence level, quality assurance, and reliability. In addition, decision algorithms are designed and developed by domain experts and system integrators do not have control over the performance of low level components. Hence, there is a compelling need for a scalable framework, which accelerates the system integration and at the same time is conform to safety levels. This position paper presents a Quality of Service (QoS) based component architecture so as to address this problem. Our approach introduces a component model that segregates the functional and non-functional aspects of decision making.

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.

International audience; In multimodal human-robot interaction (HRI), the process of communication can be established through verbal, non-verbal, and/or para-verbal cues. The linguistic literature shows that para-verbal and non-verbal communications are naturally synchronized, however the natural mechnisam of this synchronization is still largely unexplored. This research focuses on the relation between non-verbal and para-verbal communication by mapping prosody cues to the corresponding metaphoric arm gestures. Our approach for synthesizing arm gestures uses the coupled hidden Markov models (CHMM), which could be seen as a collection of HMM characterizing the segmented prosodic characteristics' stream and the segmented rotation characteristics' streams of the two arms articulations. Experimental results with Nao robot are reported.

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.