Live human-robot interactive public demonstrations with automatic emotion and personality prediction

Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism

Affect-aware socially assistive robotics (SAR) has shown great potential for augmenting interventions for children with autism spectrum disorders (ASD). However, current SAR cannot yet perceive the unique and diverse set of atypical cognitive-affective behaviors from children with ASD in an automatic and personalized fashion in long-term (multi-session) real-world interactions. To bridge this gap, this work designed and validated personalized models of arousal and valence for children with ASD using a multi-session in-home dataset of SAR interventions. By training machine learning (ML) algorithms with supervised domain adaptation (s-DA), the personalized models were able to trade off between the limited individual data and the more abundant less personal data pooled from other study participants. We evaluated the effects of personalization on a long-term multimodal dataset consisting of 4 children with ASD with a total of 19 sessions, and derived inter-rater reliability (IR) scores for binary arousal (IR = 83%) and valence (IR = 81%) labels between human annotators. Our results show that personalized Gradient Boosted Decision Trees (XGBoost) models with s-DA outperformed two non-personalized individualized and generic model baselines not only on the weighted average of all sessions, but also statistically ( p <.05) across individual sessions. This work paves the way for the development of personalized autonomous SAR systems tailored toward individuals with atypical cognitive-affective and socio-emotional needs.

Mind Your Manners! A Dataset and a Continual Learning Approach for Assessing Social Appropriateness of Robot Actions

To date, endowing robots with an ability to assess social appropriateness of their actions has not been possible. This has been mainly due to (i) the lack of relevant and labelled data and (ii) the lack of formulations of this as a lifelong learning problem. In this paper, we address these two issues. We first introduce the Socially Appropriate Domestic Robot Actions dataset (MANNERS-DB), which contains appropriateness labels of robot actions annotated by humans. Secondly, we train and evaluate a baseline Multi Layer Perceptron and a Bayesian Neural Network (BNN) that estimate social appropriateness of actions in MANNERS-DB. Finally, we formulate learning social appropriateness of actions as a continual learning problem using the uncertainty of Bayesian Neural Network parameters. The experimental results show that the social appropriateness of robot actions can be predicted with a satisfactory level of precision. To facilitate reproducibility and further progress in this area, MANNERS-DB, the trained models and the relevant code are made publicly available at https://github.com/jonastjoms/MANNERS-DB.

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

The Facial Action Coding System for Characterization of Human Affective Response to Consumer Product-Based Stimuli: A Systematic Review

To characterize human emotions, researchers have increasingly utilized Automatic Facial Expression Analysis (AFEA), which automates the Facial Action Coding System (FACS) and translates the facial muscular positioning into the basic universal emotions. There is broad interest in the application of FACS for assessing consumer expressions as an indication of emotions to consumer product-stimuli. However, the translation of FACS to characterization of emotions is elusive in the literature. The aim of this systematic review is to give an overview of how FACS has been used to investigate human emotional behavior to consumer product-based stimuli. The search was limited to studies published in English after 1978, conducted on humans, using FACS or its action units to investigate affect, where emotional response is elicited by consumer product-based stimuli evoking at least one of the five senses. The search resulted in an initial total of 1,935 records, of which 55 studies were extracted and categorized based on the outcomes of interest including (i) method of FACS implementation; (ii) purpose of study; (iii) consumer product-based stimuli used; and (iv) measures of affect validation. Most studies implemented FACS manually (73%) to develop products and/or software (20%) and used consumer product-based stimuli that had known and/or defined capacity to evoke a particular affective response, such as films and/or movie clips (20%); minimal attention was paid to consumer products with low levels of emotional competence or with unknown affective impact. The vast majority of studies (53%) did not validate FACS-determined affect and, of the validation measures that were used, most tended to be discontinuous in nature and only captured affect as it holistically related to an experience. This review illuminated some inconsistencies in how FACS is carried out as well as how emotional response is inferred from facial muscle activation. This may prompt researchers to consider measuring the total consumer experience by employing a variety of methodologies in addition to FACS and its emotion-based interpretation guide. Such strategies may better conceptualize consumers' experience with products of low, unknown, and/or undefined capacity to evoke an affective response such as product prototypes, line extensions, etc.

An interactive system for humanoid robot SHFR-III

The natural interaction between human and robot is full of challenges but indispensable. In this article, a human–robot interactive system is designed for humanoid robot SHFR-III. The system consists of three subsystems: multi-sensor positioning subsystem, emotional interaction subsystem, and dialogue subsystem. The multi-sensor positioning subsystem is designed to improve the positioning accuracy, the emotional interaction subsystem uses bimodal emotional recognition model and fuzzy emotional decision-making model to realize the emotion recognition and expression feedback to the interactive objects, and the dialogue subsystem with personal information can complete the response consistent with the default information and avoid conflicting responses .The experimental results show that the multi-sensor positioning subsystem has good environmental adaptability and positioning accuracy, the emotional interaction subsystem can achieve human-like emotional feedback, and the dialogue subsystem can achieve more natural, logical, and consistent responses.

Visual and Thermal Image Processing for Facial Specific Landmark Detection to Infer Emotions in a Child-Robot Interaction

Child-Robot Interaction (CRI) has become increasingly addressed in research and applications. This work proposes a system for emotion recognition in children, recording facial images by both visual (RGB-red, green and blue) and Infrared Thermal Imaging (IRTI) cameras. For this purpose, the Viola-Jones algorithm is used on color images to detect facial regions of interest (ROIs), which are transferred to the thermal camera plane by multiplying a homography matrix obtained through the calibration process of the camera system. As a novelty, we propose to compute the error probability for each ROI located over thermal images, using a reference frame manually marked by a trained expert, in order to choose that ROI better placed according to the expert criteria. Then, this selected ROI is used to relocate the other ROIs, increasing the concordance with respect to the reference manual annotations. Afterwards, other methods for feature extraction, dimensionality reduction through Principal Component Analysis (PCA) and pattern classification by Linear Discriminant Analysis (LDA) are applied to infer emotions. The results show that our approach for ROI locations may track facial landmarks with significant low errors with respect to the traditional Viola-Jones algorithm. These ROIs have shown to be relevant for recognition of five emotions, specifically disgust, fear, happiness, sadness, and surprise, with our recognition system based on PCA and LDA achieving mean accuracy (ACC) and Kappa values of 85.75% and 81.84%, respectively. As a second stage, the proposed recognition system was trained with a dataset of thermal images, collected on 28 typically developing children, in order to infer one of five basic emotions (disgust, fear, happiness, sadness, and surprise) during a child-robot interaction. The results show that our system can be integrated to a social robot to infer child emotions during a child-robot interaction.

From social brains to social robots: applying neurocognitive insights to human-robot interaction

Amidst the fourth industrial revolution, social robots are resolutely moving from fiction to reality. With sophisticated artificial agents becoming ever more ubiquitous in daily life, researchers across different fields are grappling with the questions concerning how humans perceive and interact with these agents and the extent to which the human brain incorporates intelligent machines into our social milieu. This theme issue surveys and discusses the latest findings, current challenges and future directions in neuroscience- and psychology-inspired human-robot interaction (HRI). Critical questions are explored from a transdisciplinary perspective centred around four core topics in HRI: technical solutions for HRI, development and learning for HRI, robots as a tool to study social cognition, and moral and ethical implications of HRI. Integrating findings from diverse but complementary research fields, including social and cognitive neurosciences, psychology, artificial intelligence and robotics, the contributions showcase ways in which research from disciplines spanning biological sciences, social sciences and technology deepen our understanding of the potential and limits of robotic agents in human social life. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.