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Robot Assisted Therapy is a new paradigm in many therapies such as the therapy of children with autism spectrum disorder. In this paper we present the use of a parrot-like robot as an assistive tool in turn taking therapy. The therapy is designed in the form of a card game between a child with autism and a therapist or the robot. The intervention was implemented in a single subject study format and the effect sizes for different turn taking variables are calculated. The results show that the child robot interaction had larger effect size than the child trainer effect size in most of the turn taking variables. Furthermore the therapist point of view on the proposed Robot Assisted Therapy is evaluated using a questionnaire. The therapist believes that the robot is appealing to children which may ease the therapy process. The therapist suggested to add other functionalities and games to let children with autism to learn more turn taking tasks and better generalize the learned tasks
Autism spectrum disorder (ASD) is a developmental disorder that influences the communication and social behavior of a person in a way that those in the spectrum have difficulty in perceiving other peoples facial expressions, as well as presenting and
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation policies on legged robots can be challenging due to their complex dynami
Robot task execution when situated in real-world environments is fragile. As such, robot architectures must rely on robust error recovery, adding non-trivial complexity to highly-complex robot systems. To handle this complexity in development, we int
With the fast development of network information technology, more and more people are immersed in the virtual community environment brought by the network, ignoring the social interaction in real life. The consequent urban autism problem has become m
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge. In this paper, we present a deep learning scheme