How Do You Like Me in This: User Embodiment Preferences for Companion Agents

We investigate the relationship between the embodiment of an artificial companion and user perception and interaction with it. In a Wizard of Oz study, 42 users interacted with one of two embodiments: a physical robot or a virtual agent on a screen through a role-play of secretarial tasks in an office, with the companion providing essential assistance. Findings showed that participants in both condition groups when given the choice would prefer to interact with the robot companion, mainly for its greater physical or social presence. Subjects also found the robot less annoying and talked to it more naturally. However, this preference for the robotic embodiment is not reflected in the users’ actual rating of the companion or their interaction with it. We reflect on this contradiction and conclude that in a task-based context a user focuses much more on a companion’s behaviour than its embodiment. This underlines the feasibility of our efforts in creating companions that migrate between embodiments while maintaining a consistent identity from the user’s point of view

Affect and believability in game characters:a review of the use of affective computing in games

Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions

Automatic Replication of Teleoperator Head Movements and Facial Expressions on a Humanoid Robot

Robotic telepresence aims to create a physical presence for a remotely located human (teleoperator) by reproducing their verbal and nonverbal behaviours (e.g. speech, gestures, facial expressions) on a robotic platform. In this work, we propose a novel teleoperation system that combines the replication of facial expressions of emotions (neutral, disgust, happiness, and surprise) and head movements on the fly on the humanoid robot Nao. Robots' expression of emotions is constrained by their physical and behavioural capabilities. As the Nao robot has a static face, we use the LEDs located around its eyes to reproduce the teleoperator expressions of emotions. Using a web camera, we computationally detect the facial action units and measure the head pose of the operator. The emotion to be replicated is inferred from the detected action units by a neural network. Simultaneously, the measured head motion is smoothed and bounded to the robot's physical limits by applying a constrained-state Kalman filter. In order to evaluate the proposed system, we conducted a user study by asking 28 participants to use the replication system by displaying facial expressions and head movements while being recorded by a web camera. Subsequently, 18 external observers viewed the recorded clips via an online survey and assessed the quality of the robot's replication of the participants' behaviours. Our results show that the proposed teleoperation system can successfully communicate emotions and head movements, resulting in a high agreement among the external observers (ICC_E = 0.91, ICC_HP = 0.72).This work was funded by the EPSRC under its IDEAS Factory Sandpits call on Digital Personhood (Grant Ref· EP/L00416X/1)

A study on the role of affective feedback in robot-assisted learning

In recent years, there have been many approaches to using robots to teach computer programming. In intelligent tutoring systems and computer-aided learning, there is also some research to show that affective feedback to the student increases learning efficiency. However, a few studies on the role of incorporating an emotional personality in the robot in robot-assisted learning have found different results. To explore this issue further, we conducted a pilot study to investigate the effect of positive verbal encouragement and non-verbal emotive behaviour of the Miro-E robot during a robot-assisted programming session. The participants were tasked to program the robot’s behaviour. In the experimental group, the robot monitored the participants’ emotional state via their facial expressions, and provided affective feedback to the participants after completing each task. In the control group, the robot responded in a neutral way. The participants filled out a questionnaire before and after the programming session. The results show a positive reaction of the participants to the robot and the exercise. Though the number of participants was small, as the experiment was conducted during the pandemic, a qualitative analysis of the data was carried out. We found that the greatest affective outcome of the session was for students who had little experience or interest in programming before. We also found that the affective expressions of the robot had a negative impact on its likeability, revealing vestiges of the uncanny valley effect

Emotion Attribution to a Non-Humanoid Robot in Different Social Situations

In the last few years there was an increasing interest in building companion robots that interact in a socially acceptable way with humans. In order to interact in a meaningful way a robot has to convey intentionality and emotions of some sort in order to increase believability. We suggest that human-robot interaction should be considered as a specific form of inter-specific interaction and that human–animal interaction can provide a useful biological model for designing social robots. Dogs can provide a promising biological model since during the domestication process dogs were able to adapt to the human environment and to participate in complex social interactions. In this observational study we propose to design emotionally expressive behaviour of robots using the behaviour of dogs as inspiration and to test these dog-inspired robots with humans in inter-specific context. In two experiments (wizard-of-oz scenarios) we examined humans' ability to recognize two basic and a secondary emotion expressed by a robot. In Experiment 1 we provided our companion robot with two kinds of emotional behaviour (“happiness” and “fear”), and studied whether people attribute the appropriate emotion to the robot, and interact with it accordingly. In Experiment 2 we investigated whether participants tend to attribute guilty behaviour to a robot in a relevant context by examining whether relying on the robot's greeting behaviour human participants can detect if the robot transgressed a predetermined rule. Results of Experiment 1 showed that people readily attribute emotions to a social robot and interact with it in accordance with the expressed emotional behaviour. Results of Experiment 2 showed that people are able to recognize if the robot transgressed on the basis of its greeting behaviour. In summary, our findings showed that dog-inspired behaviour is a suitable medium for making people attribute emotional states to a non-humanoid robot

Socially assistive robots : the specific case of the NAO

Numerous researches have studied the development of robotics, especially socially assistive robots (SAR), including the NAO robot. This small humanoid robot has a great potential in social assistance. The NAO robot’s features and capabilities, such as motricity, functionality, and affective capacities, have been studied in various contexts. The principal aim of this study is to gather every research that has been done using this robot to see how the NAO can be used and what could be its potential as a SAR. Articles using the NAO in any situation were found searching PSYCHINFO, Computer and Applied Sciences Complete and ACM Digital Library databases. The main inclusion criterion was that studies had to use the NAO robot. Studies comparing it with other robots or intervention programs were also included. Articles about technical improvements were excluded since they did not involve concrete utilisation of the NAO. Also, duplicates and articles with an important lack of information on sample were excluded. A total of 51 publications (1895 participants) were included in the review. Six categories were defined: social interactions, affectivity, intervention, assisted teaching, mild cognitive impairment/dementia, and autism/intellectual disability. A great majority of the findings are positive concerning the NAO robot. Its multimodality makes it a SAR with potential

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field