Towards spatial methods for socially assistive robotics: validation with children with autism spectrum disorders

Socially Assistive Robotics (SAR) defines the research regarding robots which provide assistance to users through social interaction [Feil-Seifer and Matari´ 2005]. Socially assistive robots are being studied for therapeutic use with children with autism spectrum disorders (ASD). It has been observed that children with ASD interact with robots differently than with people or toys. This may indicate an intrinsic interest in such machines, which could be applied as a robot augmentation for an intervention for children with ASD. Preliminary studies suggest that robots may act as intrinsicallyrewarding social partners for children with autism. However, enabling a robot to understand social behavior, and do so while interacting with the child, is a challenging problem. Children are highly individual and thus technology used for social interaction requires recognition of a wide-range of social behavior. This work addresses the challenge of designing behaviors for socially assistive robots in order to enable them to recognize and appropriately respond to a childs free-form behavior in unstructured play contexts. The focus on free-form behavior is inspired by and grounded in existing approaches to therapeutic intervention with children with ASD. This model emphasizes creating circles of communication and fostering engagement through play. A key aspect of this approach is to recognize social behavior and use engagements to bolster social interaction behavior, and to study the ethical implications of therapeutic robotics applications. This research will present a methodology and a validated experimental framework for enabling fully autonomous robots to interact with both typically developing children and children with autism spectrum disorders (ASD) in undirected scenarios using socially appropriate behavior especially where spatial interaction is concerned. This work holds autonomous operation as a critical aspect of the development and implementation of a robot system. Save for safety interventions by a human operator, the robot system presented in this work acts of its own accord. The methodology of this work holds that free-form interaction is best served by allowing a child to move about a space as they choose, and we wish to enable a robot that can allow for such freedom and function effectively for its interaction goals. As such, the robot and child interact, in part, though distance-oriented behavior, and the robot must be able to recognize those behaviors and appropriately respond to them. An overarching goal of this work is to develop a methodology which did not preclude human-human interaction, and in fact encourages human-human interaction. We wish to use this system was to be used as an augmentation, rather than a replacement for a human therapist. There is no substitute for human-human interaction in social interaction. However, the compelling interaction between children with ASD and robots is encouraging for their use as a therapeutic aid. This work aims for the following with an eye toward therapeutic potential: • Detection and mitigation of a childs distress: we define a methodology for learning and applying a datadriven spatio-temporal model of social behavior based on proxemic features to automatically differentiate between typical child-robot interactive behavior and behavior that would suggest an aversive response. Using a Gaussian Mixture Model learned over proxemic feature data the developed system is able to detect and interpret social behavior of the child with sufficient accuracy to recognize distress on the part of the child. The robot uses this model to change its own behavior to encourage positive social interaction [Feil-Seifer and Matari´ c,