Data-driven interaction methods for socially assistive Robotics: validation with children with Autism spectrum disorders

摘要: There exists a great untapped potential for the use of intelligent robots as therapeutic social partners for children. 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 argues for data-driven methods that capture the relevant range of interactions. This work addresses the challenge of designing data-driven behaviors for socially assistive robots in order to enable them to recognize and appropriately respond to a child's free-form behavior in unstructured play contexts. The focus on free-form behavior is inspired by and grounded in the DIR/Floortime approach to therapeutic intervention with children with autism spectrum disorders (ASD). This approach emphasizes fostering engagement through play …