Paper Info
Title
Pomdp-Based Coding Of Child-Robot Interaction Within A Robot-Assisted Asd Diagnostic Protocol
Abstract
The existing procedures for autism spectrum disorder (ASD) diagnosis are often time consuming and tiresome both for highly-trained human evaluators and children, which may be alleviated by using humanoid robots in the diagnostic process. Hence, this paper proposes a framework for robot-assisted ASD evaluation based on partially observable Markov decision process (POMDP) modeling, specifically POMDPs with mixed observability (MOMDPs). POMDP is broadly used for modeling optimal sequential decision making tasks under uncertainty. Spurred by the widely accepted autism diagnostic observation schedule (ADOS), we emulate ADOS through four tasks, whose models incorporate observations of multiple social cues such as eye contact, gestures and utterances. Relying only on those observations, the robot provides an assessment of the child's ASD-relevant functioning level (which is partially observable) within a particular task and provides human evaluators with readable information by partitioning its belief space. Finally, we evaluate the proposed MOMDP task models and demonstrate that chaining the tasks provides fine-grained outcome quantification, which could also increase the appeal of robot-assisted diagnostic protocols in the future.
Year
DOI
Venue
2018
10.1142/S0219843618500111
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS
Keywords
Field
DocType
Robotics, POMDP, autism spectrum disorder, diagnostics
Autism Diagnostic Observation Schedule,Computer vision,Chaining,Gesture,Computer science,Partially observable Markov decision process,Coding (social sciences),Artificial intelligence,Robot,Machine learning,Robotics,Humanoid robot
Journal
Volume
Issue
ISSN
15
2
0219-8436
Citations 
PageRank 
References 
4
0.53
5
Authors
3
Name
Order
Citations
PageRank
Frano Petric184.69
Damjan Miklic2366.92
Zdenko Kovacic35915.27