Socially Assistive Robots for Older Adults and People with Autism: An Overview

Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.This research was funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Design of a Huggable Social Robot with Affective Expressions Using Projected Images

We introduce Pepita, a caricatured huggable robot capable of sensing and conveying affective expressions by means of tangible gesture recognition and projected avatars. This study covers the design criteria, implementation and performance evaluation of the different characteristics of the form and function of this robot. The evaluation involves: (1) the exploratory study of the different features of the device, (2) design and performance evaluation of sensors for affective interaction employing touch, and (3) design and implementation of affective feedback using projected avatars. Results showed that the hug detection worked well for the intended application and the affective expressions made with projected avatars were appropriated for this robot. The questionnaires analyzing users’ perception provide us with insights to guide the future designs of similar interfaces

Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability

Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child–robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities

‘Hey robot, please step back!’ - exploration of a spatial threshold of comfort for human-mechanoid spatial interaction in a hallway scenario

Within the scope of the current research the goal was to develop an autonomous transport assistant for hospitals. As a sort of social robots, they need to fulfill two main requirements with respect to their interactive behavior with humans: (1) a high level of safety and (2) a behavior that is perceived as socially proper. One important element includes the characteristics of movement. However, state-of-the-art hospital robots rather focus on safe but not smart maneuvering. Vital motion parameters in human everyday environment are personal space and velocity. The relevance of these parameters has also been reported in existing human-robot interaction research. However, to date, no minimal accepted frontal and lateral distances for human-mechanoid proxemics have been explored. The present work attempts to gain insights into a potential threshold of comfort and additionally, aims to explore a potential interaction of this threshold and the mechanoid's velocity. Therefore, a user study putting the users in control of the mechanoid was conducted in a laboratory hallway-like setting. Findings align with previously reported personal space zones in human-robot interaction research. Minimal accepted frontal and lateral distances were obtained. Furthermore, insights into a potential categorization of the lateral personal space area around a human are discussed for human-robot interaction

The Effects of the Big Five Personality Traits on Stress among Robot Programming Students

This paper presents relationships between personality traits and stress levels in light of the transactional model of stress. The framework of the transactional model was applied to determine the significance of work with a robot for primary and secondary stress appraisal made by an individual. We decided to use the Big Five personality traits model as one which integrates the dimensions of personality and had been previously applied to research on stress. The participants in our three-wave study were 105 students doing an industrial robots programming course. Using Ten Item Personality Inventory (TIPI) and Questionnaire for Primary and Secondary Appraisal (PASA) questionnaires, we gathered information about the students’ personality, the level of anticipated stress, and the stress experienced while working with a robot after 6 and 12 weeks. The obtained results prove that emotional stability is significant for secondary appraisal of anticipated stress. The results also show that openness to experience is a negative predictor, whereas conscientiousness is a positive predictor of primary stress appraisal. The ability to cope with stress after 12 weeks of work with a robot is appraised as higher by older, more conscientious, and introverted people. The obtained results are discussed from the psychological perspective of stress and personality, which complements earlier studies in technical sciences. The limitations of the study are also indicated

An Open-Source Social Robot Based on Compliant Soft Robotics for Therapy with Children with ASD

Therapy with robotic tools is a promising way to help improve verbal and nonverbal communication in children. The robotic tools are able to increase aspects such as eye contact and the ability to follow instructions and to empathize with others. This work presents the design methodology, development, and experimental validation of a novel social robot based on CompliAnt SofT Robotics called the CASTOR robot, which intends to be used as an open-source platform for the long-term therapy of children with autism spectrum disorder (CwASD). CASTOR integrates the concepts of soft actuators and compliant mechanisms to create a replicable robotic platform aimed at real therapy scenarios involving physical interaction between the children and the robot. The validation shows promising results in terms of robustness and the safety of the user and robot. Likewise, mechanical tests assess the robot’s response to blocking conditions for two critical modules (i.e., neck and arm) in interaction scenarios. Future works should focus on the validation of the robot’s effectiveness in the therapy of CwASD.</jats:p

A statistical approach to a verb vector task classifier

A thesis submitted to the University of Bedfordshire, in fulfilment ofthe requirements for the degree of Master of Science by researchHow to enable a service robot to understand its user's intention is a hot topic of research today. Based on its understanding, the robot can coordinate and adjust its behaviours to provide desired assistance and services to the user as a capable partner. Active Robot Learning (ARL) is an approach to the development of the understanding of human intention. The task action bank is part of the ARL which can store task categories. In this approach, a robot actively performs test actions in order to obtain its user's intention from the user's response to the action. This thesis presents an approach to verbs clustering based on the basic action required of the robot, using a statistical method. A parser is established to process a corpus and analyse the probability of the verb feature vector, for example when the user says "bring me a cup of coffee", this means the same as "give me a cup of coffee". This parser could identify similar verbs between "bring" and "give" with the statistical method. Experimental results show the collocation between semantically related verbs, which can be further utilised to establish a test action bank for Active Robot Learning (ARL)