Emotion attribution to a non-humanoid robot in different social situations

How do you do the things that you do? Ethological approach to the description of robot behaviour

The detailed description of behaviour of the interacting parties is becoming more and more important in human-robot interaction (HRI), especially in social robotics (SR). With the rise in the number of publications, there is a substantial need for the objective and comprehensive description of implemented robot behaviours to ensure comparability and reproducibility of the studies. Ethograms and the meticulous analysis of behaviour was introduced long ago in animal behaviour research (cf. ethology). The adoption of this method in SR and HRI can ensure the desired clarity over robot behaviours, while also providing added benefits during robot development, behaviour modelling and analysis of HRI experiments. We provide an overview of the possible uses and advantages of ethograms in HRI, and propose a general framework for describing behaviour which can be adapted to the requirements of specific studies.

Interactive neurorobotics: Behavioral and neural dynamics of agent interactions

Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. This research seeks to study how organisms respond to artificial agents in contrast to biological or inanimate ones. This experiment uses the novel affordances of the robotic platforms to investigate complex dynamics during minimally structured interactions that would be difficult to capture with classical experimental setups. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat-robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

Emotional State Analysis Model of Humanoid Robot in Human-Computer Interaction Process

The traditional humanoid robot dialogue system is generally based on template construction, which can make a good response in the set dialogue domain but cannot generate a good response to the content outside the domain. The rules of the dialogue system rely on manual design and lack of emotion detection of the interactive objects. In view of the shortcomings of traditional methods, this study designed an emotion analysis model based on deep neural network to detect the emotion of interactive objects and built an open-domain dialogue system of humanoid robot. In affective state analysis language processing, language coding, feature analysis, and Word2vec research are carried out. Then, an emotional state analysis model is constructed to train the emotional state of a humanoid robot, and the training results are summarized.

Mental State Attribution to Robots: A Systematic Review of Conceptions, Methods, and Findings

The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far exhibit considerable diversity in terms of how the phenomenon is described and how it is approached from a theoretical and methodological standpoint. This literature review addresses the need for a shared scientific understanding of mental state attribution to robots by systematically and comprehensively collating conceptions, methods, and findings from 155 empirical studies across multiple disciplines. The findings of the review include that: (1) the terminology used to describe mental state attribution to robots is diverse but largely homogenous in usage; (2) the tendency to attribute mental states to robots is determined by factors such as the age and motivation of the human as well as the behavior, appearance, and identity of the robot; (3) there is a computer < robot < human pattern in the tendency to attribute mental states that appears to be moderated by the presence of socially interactive behavior; (4) there are conflicting findings in the empirical literature that stem from different sources of evidence, including self-report and non-verbal behavioral or neurological data. The review contributes toward more cumulative research on the topic and opens up for a transdisciplinary discussion about the nature of the phenomenon and what types of research methods are appropriate for investigation.

Human, Animal and Automata Attributions: an Investigation of the Multidimensionality of the Ontologization Process

The ontologization process involves the use of social representation relating to the human–animal binary to classify ingroup and outgroup members. To date, no study has investigated the multidimensional nature (i.e. human, animal and automata) of the ontologizing process via structural equation modelling (SEM). Four hundred and twenty-one Italian participants were asked to attribute 24 positive/negative, human/animal/automata associates to each of three target groups: typical Roma/Chinese/Italian. Results showed that the proposed six-factor model (i.e. positive/negative, human/animal/automata essence) was statistically robust for each of the three groups. The Roma group was animalized by attributing more animal negative associates than any other target group, whereas the Chinese group was mainly given a robot positive essence.

The Zoomorphic Miro Robot's Affective Expression Design and Perceived Appearance

This article proposes design guidelines for 11 affective expressions for the Miro robot, and evaluates the expressions through an online video study with 116 participants. All expressions were recognized significantly above the chance level. For six of the expressions, the correct response was selected significantly more than the others, while more than one emotion was associated to some other expressions. Design decisions and the robot’s limitations that led to selecting other expressions, along with the correct expression, are discussed. We also investigated how participants’ abilities to recognize human and animal emotions, their tendency to anthropomorphize, and their familiarity with and attitudes towards animals and pets might have influenced the recognition of the robot’s affective expressions. Results show significant impact of human emotion recognition, difficulty in understanding animal emotions, and anthropomorphism tendency on recognition of Robot’s expressions. We did not find such effects regarding familiarity with/attitudes towards animals/pets in terms of how they influenced participants’ recognition of the designed affective expressions. We further studied how the robot is perceived in general and showed that it is mostly perceived to be gender neutral, and, while it is often associated with a dog or a rabbit, it can also be perceived as a variety of other animals.

Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review

Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribute emotions and personality, assign social competencies, and develop a long-lasting attachment to robots. However, without a clear theoretical framework-building on biological, psychological, and technological knowledge-current societal demands for establishing successful human-robot attachment (HRA) as a new form of inter-species interactions might fail. The study of evolutionarily adaptive animal behavior (i.e., ethology) suggests that human-animal behaviors can be considered as a plausible solution in designing and building models of ethorobots-including modeling the inter-species bond between domesticated animals and humans. Evidence shows that people assign emotional feelings and personality characteristics to animal species leading to cooperation and communication-crucial for designing social robots such as companion robots. Because dogs have excellent social skills with humans, current research applies human-dog relationships as a template to understand HRA. Our goal of this article is twofold. First, we overview the research on how human-dog interactions are implemented as prototypes of non-human social companions in HRA. Second, we review research about attitudes that humans have for interacting with robotic dogs based on their appearance and behavior, the implications for forming attachments, and human-animal interactions in the rising sphere of robot-assisted therapy. The rationale for this review is to provide a new perspective to facilitate future research among biologists, psychologists, and engineers-contributing to the creation of innovative research practices for studying social behaviors and its implications for society addressing HRA.

Ethorobotics: A New Approach to Human-Robot Relationship

Here we aim to lay the theoretical foundations of human-robot relationship drawing upon insights from disciplines that govern relevant human behaviors: ecology and ethology. We show how the paradox of the so called “uncanny valley hypothesis” can be solved by applying the “niche” concept to social robots, and relying on the natural behavior of humans. Instead of striving to build human-like social robots, engineers should construct robots that are able to maximize their performance in their niche (being optimal for some specific functions), and if they are endowed with appropriate form of social competence then humans will eventually interact with them independent of their embodiment. This new discipline, which we call ethorobotics, could change social robotics, giving a boost to new technical approaches and applications.