Early research by Prof. Maja Matarić and doctoral student David Feil-Seifer of the USC Interaction Laboratory confirms what has been widely reported anecdotally; that children with Autism Spectrum Disorders (ASD) often interact more easily with mechanical devices and robots than with unfamiliar people.
Bubblebot: When set in "contingent" behavior mode, children's actions can control his actions.
The study tested whether interaction, as opposed to simple passive observation, occurred between children with ASD and a colorful wheeled bubble-blowing robot.
The researchers found that the behavior of the robot affected the social behavior of children with ASD, both in terms of how they interacted with the robot and with the parent also present in the room. Generally, when the robot was acting contingently, in response to the child, the child was more sociable with the robot and with the parent.
The total amount of speech of the participating children went from 39.4 to 48.4 utterances, speech aimed at the robot from 6.2 to 6.6 utterances, and speech aimed at the parent from 17.8 to 33 utterances. Total interactions with the robot went from 43.42 to 55.31, with button pushes increasing from 14.69 to 21.87 and other interactions going from 24.11 to 28. Total directed interactions (interactions that were clearly directed at either the robot or the parent) went up from 62.75 to 89.47.
Key collaborators working with Matarić and Feil-Seifer include Prof. Shri Narayanan from Viterbi School's Ming Shieh Department of Electrical Engineering, Dr. Marian Williams, Dr. Michele Kipke and Prof. Clara Lajonchere, all from CHLA. Together, the interdisciplinary team is developing interactive intelligent robots toward aiding ASD diagnosis and treatment.
Matarić has for years been working in the field of socially assisted robots to help a variety of other user populations, including stroke survivers and patients with Alzheimer’s Disease. She notes that ASD is now at "epidemic" proportions in the United States and that the children’s interest in robots presents a valuable opportunity to both study the disorder and develop innovative treatments.
The research was funded by the USC Provost's Center for Interdisciplinary Research, the Okawa Foundation, and an NSF Computing Research Infrastructure Grant.