Emotional robot to examine different play patterns and affective responses of children with and without ASD

Boccanfuso, Laura; Barney, Erin; Foster, Claire; Ahn, Yeojin Amy; Chawarska, Katarzyna; Scassellati, Brian; Shic, Frederick

doi:10.1109/hri.2016.7451729

Cited by 52 publications

(35 citation statements)

References 35 publications

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“…Children interacting with social robots are prone to touching the robot. In some cases, they might show aggression toward the robot [1,11,107]. This requires that the existing design guidelines must ensure the safety of the children and the physical integrity of the robot, especially during meltdowns [1,16,17].…”

Section: Robots and Potential Risksmentioning

confidence: 99%

Head Impact Severity Measures for Small Social Robots Thrown During Meltdown in Autism

Alhaddad

Cabibihan

Bonarini

2018

Int J of Soc Robotics

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Social robots have gained a lot of attention recently as they have been reported to be effective in supporting therapeutic services for children with autism. However, children with autism may exhibit a multitude of challenging behaviors that could be harmful to themselves and to others around them. Furthermore, social robots are meant to be companions and to elicit certain social behaviors. Hence, the presence of a social robot during the occurrence of challenging behaviors might increase any potential harm. In this paper, we identified harmful scenarios that might emanate between a child and a social robot due to the manifestation of challenging behaviors. We then quantified the harm levels based on severity indices for one of the challenging behaviors (i.e. throwing of objects). Our results showed that the overall harm levels based on the selected severity indices are relatively low compared to their respective thresholds. However, our investigation of harm due to throwing of a small social robot to the head revealed that it could potentially cause tissue injuries, subconcussive or even concussive events in extreme cases. The existence of such behaviors must be accounted for and considered when developing interactive social robots to be deployed for children with autism.

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Section: Robots and Potential Risksmentioning

confidence: 99%

Head Impact Severity Measures for Small Social Robots Thrown During Meltdown in Autism

Alhaddad

Cabibihan

Bonarini

2018

Int J of Soc Robotics

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“…While the tracking itself helps tremendously in producing realistic virtual avatar faces, there is still an open research issue in how to accurately understand the captured motion with respect to nonverbal communicative behaviors of a participant with the exception of eye gaze (and to some extend facial expressions). Hence, the state of the art practice is still to record the video of the participant and then use domain experts to analyze the video regarding the participant's nonverbal communicative behaviors [52,[118][119][120].…”

Section: Facial Motion/expression Trackingmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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“…A few researchers have explored various areas where positive reinforcement from robots had a large impact on children. Boccanfuso et al [4] investigated the difference in responses between children with or without autism with an emotion-stimulating robot using positive reinforcement in an interactive environment. Nunez et al [30] described the use of positive reinforcers to overcome the underlying challenges in motivating a child to continue learning and to share the experience with others.…”

Section: Empathy and Positive Reinforcersmentioning

confidence: 99%

Mutual Reinforcement Learning with Robot Trainers

Roy

Kieson

Abramson

et al. 2019

2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

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Recently, collaborative robots have begun to train humans to achieve complex tasks, and the mutual information exchange between them can lead to successful robot-human collaborations. In this paper we demonstrate the application and effectiveness of a new approach called mutual reinforcement learning (MRL), where both humans and autonomous agents act as reinforcement learners in a skill transfer scenario over continuous communication and feedback. An autonomous agent initially acts as an instructor who can teach a novice human participant complex skills using the MRL strategy. While teaching skills in a physical (block-building) (n = 34) or simulated (Tetris) environment (n = 31), the expert tries to identify appropriate reward channels preferred by each individual and adapts itself accordingly using an exploration-exploitation strategy. These reward channel preferences can identify important behaviors of the human participants, because they may well exercise the same behaviors in similar situations later. In this way, skill transfer takes place between an expert system and a novice human operator. We divided the subject population into three groups and observed the skill transfer phenomenon, analyzing it with Simpson"s psychometric model. 5-point Likert scales were also used to identify the cognitive models of the human participants. We obtained a shared cognitive model which not only improves human cognition but enhances the robot's cognitive strategy to understand the mental model of its human partners while building a successful robot-human collaborative framework.

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Emotional robot to examine different play patterns and affective responses of children with and without ASD

Cited by 52 publications

References 35 publications

Head Impact Severity Measures for Small Social Robots Thrown During Meltdown in Autism

Head Impact Severity Measures for Small Social Robots Thrown During Meltdown in Autism

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Mutual Reinforcement Learning with Robot Trainers

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