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In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person’s perspective, drawing upon both social and spatial information. Children with ASD often find it difficult to understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implemented as the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot’s perspective. Children with ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally–Anne and Charlie tests) were conducted with the children. Our findings suggest that children with ASD can indeed benefit from this approach of robot-assisted therapy.

Visual Perspective Taking (VPT) is the ability to see the world from another person’s perspective, taking into account what they see and how they see it, drawing upon both spatial and social information. Children with autism often find it difficult to understand that other people might have perspectives, viewpoints, beliefs and knowledge that are different from their own, which is a fundamental aspect of VPT. In this research we aimed to develop a methodology to assist children with autism develop their VPT skills using a humanoid robot and present results from our first long-term pilot study. The games we devised were implemented with the Kaspar robot and, to our knowledge, this is the first attempt to improve the VPT skills of children with autism through playing and interacting with a humanoid robot.We describe in detail the standard pre- and post-assessments that we performed with the children in order to measure their progress and also the inclusion criteria derived fromthe results for future studies in this field. Our findings suggest that some children may benefit from this approach of learning about VPT, which shows that this approach merits further investigation.

Robot's perception is essential for performing high-level tasks such as understanding, learning, and in general, human-robot interaction (HRI). For this reason, different perception systems have been proposed for different robotic platforms in order to detect high-level features such as facial expressions and body gestures. However, due to the variety of robotics software architectures and hardware platforms, these highly customized solutions are hardly interchangeable and adaptable to different HRI contexts. In addition, most of the developed systems have one issue in common: they detect features without awareness of the real-world contexts (e.g., detection of environmental sound assuming that it belongs to a person who is speaking, or treating a face printed on a sheet of paper as belonging to a real subject). This paper presents a novel social perception system (SPS) that has been designed to address the previous issues. SPS is an out-of-the-box system that can be integrated into different robotic platforms irrespective of hardware and software specifications. SPS detects, tracks, and delivers in real-time to robots, a wide range of human- and environment- relevant features with the awareness of their real-world contexts. We tested SPS in a typical scenario of HRI for the following purposes: to demonstrate the system capability in detecting several high-level perceptual features as well as to test the system capability to be integrated into different robotics platforms. Results show the promising capability of the system in perceiving real world in different social robotics platforms, as tested in two humanoid robots, i.e., FACE and ZENO.

This paper describes a context-dependent social gaze-control system implemented as part of a humanoid social robot. The system enables the robot to direct its gaze at multiple humans who are interacting with each other and with the robot. The attention mechanism of the gaze-control system is based on features that have been proven to guide human attention: nonverbal and verbal cues, proxemics, the visual field of view, and the habituation effect. Our gaze-control system uses Kinect skeleton tracking together with speech recognition and SHORE-based facial expression recognition to implement the same features. As part of a pilot evaluation, we collected the gaze behavior of 11 participants in an eye-tracking study. We showed participants videos of two-person interactions and tracked their gaze behavior. A comparison of the human gaze behavior with the behavior of our gaze-control system running on the same videos shows that it replicated human gaze behavior 89% of the time.