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To be seamlessly integrated in human-centered environments, robots are expected to have intelligent social capabilities on top of their physical abilities. To this end, research in artifi-cial intelligence and human-robot interaction face two major challenges. Firstly, robots need to cope with uncertainty during interaction, especially when dealing with factors that are not fully observable and hard to infer (latent variables) such as the states representing the dynamic environment and human behavior (e.g., intents, goals, preferences). Secondly, robots need to communicate their behaviors to agents (humans and other robots in the environment) in a clear and understandable manner. Therefore, robots need to be context-aware: being able to perceive and understand their surroundings, and adapt their functionalities accordingly.

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.

A key question in transportation research is whether drivers show behavioral adaptation, that is, slower or faster driving, when new technology is introduced into the vehicle. This study investigates behavioral adaptation in response to the sport mode, a technology that alters the vehicle's auditory, throttle-mapping, power-steering, and chassis settings. Based on the literature, it can be hypothesized that the sport mode increases perceived sportiness and encourages faster driving. Oppositely, the sport mode may increase drivers’ perceived danger, homeostatically causing them to drive more slowly. These hypotheses were tested using an instrumented vehicle on a test track. Thirty-one drivers were asked to drive as they normally would with different sport mode settings: Baseline, Modified Throttle Mapping (MTM), Artificial Engine Sound enhancement (AESe), MTM and AESe combined (MTM-AESe), and MTM, AESe combined with four-wheel steering, increased damping, and decreased power steering (MTM-AESe-4WS). Post-trial questionnaires showed increased perceived sportiness but no differences in perceived danger for the three MTM conditions compared to Baseline. Furthermore, compared to Baseline, MTM led to higher vehicle accelerations and, with a smaller effect size, a higher time-percentage of driving above the 110 km/h speed limit, but not higher cornering speeds. The AESe condition did not significantly affect perceived sportiness, perceived danger, and driving speed compared to Baseline. These findings suggest that behavioral adaptation is a functional and opportunistic phenomenon rather than mediated by perceived sportiness or perceived danger.

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.

The human gestures occur spontaneously and usually they are aligned with speech, which leads to a natural and expressive interaction. Speech-driven gesture generation is important in order to enable a social robot to exhibit social cues and conduct a successful human-robot interaction. In this paper, the generation process involves mapping acoustic speech representation to the corresponding gestures for a humanoid robot. The paper proposes a new GAN (Generative Adversarial Network) architecture for speech to gesture generation. Instead of the fixed mapping from one speech to one gesture pattern, our end-to-end GAN structure can generate multiple mapped gestures patterns from one speech (with multiple noises) just like humans do. The generated gestures can be applied to social robots with arms. The evaluation result shows the effectiveness of our generative model for speech-driven robot gesture generation.

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; 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 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.

Human emotion detection is an important aspect in social robotics and in human-robot interaction (HRI). In this paper, we propose a vision-based multimodal emotion recognition method based on gait data and facial thermal images designed for social robots. Our method can detect four human emotional states (i.e., neutral, happiness, anger, and sadness). We gathered data from 25 participants in order to build-up an emotion database for training and testing our classification models. We implemented and tested several approaches such as Convolutional Neural Network (CNN), Hidden Markov Model (HMM), Support Vector Machine (SVM), and Random Forest (RF). These were trained and tested in order to compare the emotion recognition ability and to find the best approach. We designed a hybrid model with both the gait and the thermal data and the accuracy of our system shows an improvement of 10% over the other models based on our emotion database. This is a promising approach to be explored in a real-time human-robot interaction scenario.

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

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.

International audience; Interaction plays a critical role in skills learning for natural communication. In human-robot interaction (HRI), robots can get feedback during the interaction to improve their social abilities. In this context, we propose an interactive robot learning framework using mul-timodal data from thermal facial images and human gait data for online emotion recognition. We also propose a new decision-level fusion method for the multimodal classification using Random Forest (RF) model. Our hybrid online emotion recognition model focuses on the detection of four human emotions (i.e., neutral, happiness, angry, and sadness). After conducting offline training and testing with the hybrid model, the accuracy of the online emotion recognition system is more than 10% lower than the offline one. In order to improve our system, the human verbal feedback is injected into the robot interactive learning. With the new online emotion recognition system, a 12.5% accuracy increase compared with the online system without interactive robot learning is obtained.

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.

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.

Robotic systems are becoming more safety critical systems as they are deployed in unstructured human-centered environments. These software intensive systems are composed of distributed, heterogeneous software components interacting in a highly dynamic, uncertain environment. However, no systematic software development process is followed in robotics research. This is a real barrier for system level performance analysis and reasoning, which are in turn required for scalable bench-marking methods and reusing existing software. This chapter provides an end-to-end overview on how robotic software systems can be formally specified from requirement modeling, through solution space exploration, and architecture modeling, and finally to generate executable code. The process is based on SafeRobots framework—a model-driven toolchain for designing software for robotics. Several domain-specific modeling languages that are developed as a part of this integrated approach are also discussed.

Current predictors of fuel consumption are typically based on computer simulations or data collections in real traffic, where the route and vehicle type are not under the researcher’s control. Here, we predicted fuel consumption using test track data, an approach that allowed for location-specific predictions. Ninety-one drivers drove a total of 4617 laps, in two vehicles (Renault Megane, Renault Clio), on two routes (highway and mountain), and with two eco-driving instructions (normal and eco). A multivariate analysis at the level of laps showed a strong predictive value for metrics related to speed, RPM, and throttle position, but with a considerable amount of variance attributable to route and vehicle type. A subsequent location-specific analysis showed that the predictive correlation of driving speed and throttle position fluctuated strongly during the lap and at some locations even became negative. We conclude that there is considerable potential in instantaneous location-specific prediction of fuel consumption.

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.

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.

This paper investigates the relationship between different physiological parameters and three tasks performed by an elderly individual in a 5-days interaction scenario. The physiological parameters investigated are: the galvanic skin response, the facial temperature variation, the heart rate, and the respiration rate. The three tasks were the same during all interactions. More specifically, the participant started with a relaxation period of 3 min, continued with 5 cognitive games, and finished with a news reading task. Each day consisted of two sessions: morning session (at around 11am), and afternoon session (at around 3pm). Our hypotheses were validated, meaning that we can differentiate between the three tasks by looking only at the physiological parameters, and we can differentiate between two difficulty levels of the cognitive games. A discussion of these results is also provided.

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.

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.

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.

With the development of compliant mechanisms and tactile skins, social robots are more inclined to physically interact with humans. Touch has been proved highly beneficial in interpersonal social interaction. This motivates social robots to exhibit affective touch abilities. However, most of the studies tackle the consequences and the perception of touch instead of how touching is performed. Understanding the touch manner enables to control robots in a more rich and natural way, but also to detect the intention of the user while touching the robot. This paper focuses on one specific social interaction based on touch, the handshake, and it analyses the pressure exerted by the participants depending on their mood. A corpus of daily repeated handshakes of 11 participants, during 16 days in Human-Human and Human-Robot interactions was produced. A handshake descriptor of 6 variables was computed and allowed to discriminate the participants' behavior. One of the variables (i.e., the maximum pressure on the fingers) enabled to discriminate the mood “Bored” from the others.

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; A key factor of a successful Human-Robot Interaction (HRI) is the robot's ability to understand the messages transmitted by a human user, especially their nonverbal and paraverbal components. Our work focuses on the robot's ability to detect if a person is deceitful or honest, based on an evaluation of their physiological state and using only noninvasive techniques, such as thermal and RGB-D imaging. In particular, we currently study and evaluate several physiological manifestations during an experiment consisting of an interrogation conducted by a robot that follows a mock-up crime, then correlate them with the truth values of the participants' answers. The first results are promising.

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; 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.

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.

International audience