Robot-assisted Autism Spectrum Disorder Diagnostics using POMDPs

Abstract

The proposed research is aimed towards developing models for control and assessment of child-robot interaction within a robot-assisted autism spectrum disorder diagnostic protocol. The robot-assisted protocol contains several tasks which consist of robot actions to elicit the interaction and observations through which the robot perceives the behaviour of the child. Tasks of the protocol are modelled using the partially observable Markov decision process (POMDP) model, which enables the robot to autonomously choose actions and assess the interaction. Assessment is based on the robot belief state related to the unobservable state of the child. The robot-assisted diagnostic protocol is modelled using hierarchical POMDP model enabling the robot to autonomously select the sequence of tasks within the protocol. The effectiveness of the developed models will be experimentally evaluated through examinations with multiple children in clinical settings.