Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy

Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children’s affective states and engagement during robot-assisted autism therapy. The key to our approach is a novel shift from the traditional ML paradigm; instead of using “one-size-fitsall” ML models, our personalized ML framework is optimized for each child by leveraging relevant contextual information (demographics and behavioral