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

A framework for robot learning during child-robot interaction with human engagement as reward signal

International audienceUsing robots as therapeutic or educational tools for children with autism requires robots to be able to adapt their behavior specifically for each child with whom they interact. In particular, some children may like to be looked into the eyes by the robot while some may not. Some may like a robot with an extroverted behavior while others may prefer a more introverted behavior. Here we present an algorithm to adapt the robot's expressivity parameters of action (mutual gaze duration, hand movement expressivity) in an online manner during the interaction. The reward signal used for learning is based on an estimation of the child's mutual engagement with the robot, measured through non-verbal cues such as the child's gaze and distance from the robot. We first present a pilot joint attention task where children with autism interact with a robot whose level of expressivity is predetermined to progressively increase, and show results suggesting the need for online adaptation of expressivity. We then present the proposed learning algorithm and some promising simulations in the same task. Altogether, these results suggest a way to enable robot learning based on non-verbal cues and to cope with the high degree of non-stationarities that can occur during interaction with children

The Role of Personality Factors and Empathy in the Acceptance and Performance of a Social Robot for Psychometric Evaluations

Research and development in socially assistive robotics have produced several novel applications in the care of senior people. However, some are still unexplored such as their use as psychometric tools allowing for a quick and dependable evaluation of human users’ intellectual capacity. To fully exploit the application of a social robot as a psychometric tool, it is necessary to account for the users’ factors that might influence the interaction with a robot and the evaluation of user cognitive performance. To this end, we invited senior participants to use a prototype of a robot-led cognitive test and analyzed the influence of personality traits and user’s empathy on the cognitive performance and technology acceptance. Results show a positive influence of a personality trait, the “openness to experience”, on the human-robot interaction, and that other factors, such as anxiety, trust, and intention to use, are influencing technology acceptance and correlate the evaluation by psychometric tests

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed

What makes a social robot good at interacting with humans?

This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed

CLARA: Building a Socially Assistive Robot to Interact with Elderly People

Although the global population is aging, the proportion of potential caregivers is not keeping pace. It is necessary for society to adapt to this demographic change, and new technologies are a powerful resource for achieving this. New tools and devices can help to ease independent living and alleviate the workload of caregivers. Among them, socially assistive robots (SARs), which assist people with social interactions, are an interesting tool for caregivers thanks to their proactivity, autonomy, interaction capabilities, and adaptability. This article describes the different design and implementation phases of a SAR, the CLARA robot, both from a physical and software point of view, from 2016 to 2022. During this period, the design methodology evolved from traditional approaches based on technical feasibility to user-centered co-creative processes. The cognitive architecture of the robot, CORTEX, keeps its core idea of using an inner representation of the world to enable inter-procedural dialogue between perceptual, reactive, and deliberative modules. However, CORTEX also evolved by incorporating components that use non-functional properties to maximize efficiency through adaptability. The robot has been employed in several projects for different uses in hospitals and retirement homes. This paper describes the main outcomes of the functional and user experience evaluations of these experiments.This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116), the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 825003 (DIH-HERO SUSTAIN), the RoQME and MiRON Integrated Technical Projects funded, in turn, by the EU RobMoSys project (H20202-732410), the project RTI2018-099522-B-C41, funded by the Gobierno de España and FEDER funds, the AT17-5509-UMA and UMA18-FEDERJA-074 projects funded by the Junta de Andalucía, and the ARMORI (CEIATECH-10) and B1-2021_26 projects funded by the University of Málaga. Partial funding for open access charge: Universidad de Málaga

Improved mutual understanding for human-robot collaboration: Combining human-aware motion planning with haptic feedback devices for communicating planned trajectory

In a collaborative scenario, the communication between humans and robots is a fundamental aspect to achieve good efficiency and ergonomics in the task execution. A lot of research has been made related to enabling a robot system to understand and predict human behaviour, allowing the robot to adapt its motion to avoid collisions with human workers. Assuming the production task has a high degree of variability, the robot's movements can be difficult to predict, leading to a feeling of anxiety in the worker when the robot changes its trajectory and approaches since the worker has no information about the planned movement of the robot. Additionally, without information about the robot's movement, the human worker cannot effectively plan own activity without forcing the robot to constantly replan its movement. We propose a novel approach to communicating the robot's intentions to a human worker. The improvement to the collaboration is presented by introducing haptic feedback devices, whose task is to notify the human worker about the currently planned robot's trajectory and changes in its status. In order to verify the effectiveness of the developed human-machine interface in the conditions of a shared collaborative workspace, a user study was designed and conducted among 16 participants, whose objective was to accurately recognise the goal position of the robot during its movement. Data collected during the experiment included both objective and subjective parameters. Statistically significant results of the experiment indicated that all the participants could improve their task completion time by over 45% and generally were more subjectively satisfied when completing the task with equipped haptic feedback devices. The results also suggest the usefulness of the developed notification system since it improved users' awareness about the motion plan of the robot.Web of Science2111art. no. 367

Applying the “human-dog interaction” metaphor in human-robot interaction: a co-design practice engaging healthy retired adults in China

This research adopts a Deweyan pragmatist approach and “research through design” methods to explore the use of human-dog interaction as a model for developing human-robot interaction. This research asks two questions: (1) In what way could the human-dog interaction model inform the design of social robots to meet the needs of older adults? (2) What role could aesthetic, functional and behavioural aspects of the human-dog interaction play in older adults’ interaction with social robots? Driven by the pragmatist approach, this thesis uses the dog-human interaction model as a metaphor in this thesis. The research carried out four studies in two parts. The first part of the practice includes two explorative studies to identify aspects of human-dog interaction that could inform the design of social robots for older adults. Study 1 explores aspects of human-dog interaction that could inform the design of human-robot interaction for retired adults. Study 2 explores a group of healthy retired adults’ attitudes and preferences toward social/assistive robots in China. The findings suggest that, first, the pairing and training process provides a framework for building personalised social robots in terms of form, function, interaction, and stakeholders involved. Second, the cooperative interaction between a human and a guide dog provides insights for building social robots that take on leading roles in interactions. The robot-as-dog metaphor offers a new perspective to rethink the design process of social robots based on the role dog trainer, owner, and the dog plays in human-dog interaction. In the second part of the practice, two more studies are conducted to articulate the usefulness of the designer-as-trainer-metaphor, and the personalisation-astraining-metaphor, using participatory co-designing methods. Engaging both retired adult participants and roboticists as co-designers to investigate further how aesthetic aspects, functional features, and interactive behaviours characterising dog-human interaction could inform how older adults can interact with social robots. Study 3 involved co-designing a robot probe with roboticists and later deploying it in a participant’s home using the Wizard of Oz method. The personalisation-as-training metaphor helps facilitate a critical discussion for the interdisciplinary co-design process. It broadens the design space when addressing the technical limitation of the probe’s camera through reflection-in-action. Study 4 engages the retired adults as co-designers to envision what characteristics they would like robots to have, with attention to the robot’s form, the functions that the robot can perform and how the robot interacts with users. The study applies techniques such as sketching and storyboarding to understand how retired adults make sense of these core elements that are key to developing social/assistive robots for positive ageing. This thesis makes two main contributions to knowledge in human-robot interaction and interaction design research. Firstly, it provides an applied example using the robot-as-dog metaphor as a tool to probe human-robot interactions in a domestic context. Secondly, to show dog-human interaction model is applicable to different levels of abstraction for the co-designing process that involves the roboticists and the end-users. The outcome shows a reflective practice that engages metaphors to facilitate communication across disciplines in the co-design process

Enhance the Language Ability of Humanoid Robot NAO through Deep Learning to Interact with Autistic Children

Autism spectrum disorder (ASD) is a life-long neurological disability, and a cure has not yet been found. ASD begins early in childhood and lasts throughout a person’s life. Through early intervention, many actions can be taken to improve the quality of life of children. Robots are one of the best choices for accompanying children with autism. However, for most robots, the dialogue system uses traditional techniques to produce responses. Robots cannot produce meaningful answers when the conversations have not been recorded in a database. The main contribution of our work is the incorporation of a conversation model into an actual robot system for supporting children with autism. We present the use a neural network model as the generative conversational agent, which aimed at generating meaningful and coherent dialogue responses given the dialogue history. The proposed model shares an embedding layer between the encoding and decoding processes through adoption. The model is different from the canonical Seq2Seq model in which the encoder output is used only to set-up the initial state of the decoder to avoid favoring short and unconditional responses with high prior probability. In order to improve the sensitivity to context, we changed the input method of the model to better adapt to the utterances of children with autism. We adopted transfer learning to make the proposed model learn the characteristics of dialogue with autistic children and to solve the problem of the insufficient corpus of dialogue. Experiments showed that the proposed method was superior to the canonical Seq2sSeq model and the GAN-based dialogue model in both automatic evaluation indicators and human evaluation, including pushing the BLEU precision to 0.23, the greedy matching score to 0.69, the embedding average score to 0.82, the vector extrema score to 0.55, the skip-thought score to 0.65, the KL divergence score to 5.73, and the EMD score to 12.21