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Since it was first discovered that robots such as the turtle-like LOGO can positively affect the social interactions of children with autism [Weir and Emanuel, 1976], many researchers have studied this phenomenon from a number of different perspectives.

Keepon

In addition to the abovementioned projects, Kozima and Yano first suggested using games with robots that could establish and maintain joint attention in order to teach children with autism skills for social interaction [Kozima and Yano, 2001]. Later,

Kozima, Nakagawa, and others developed a simple snowman-like robot, Keepon, to implement these ideas. They found that the robot was capable of establishing triadic interactions between itself, a 2-4 year old child with autism, and another individual, whether another child or the parent / caregiver [Kozima et al., 2005]. Keepon was also used in a three-year longitudinal study of interacting with children with autism, during which it was found that a child who originally would not make eye contact with the robot (which was remotely operated by an experimenter) gradually drew closer while making eye contact and interacting with Keepon over the course of the study [Kozima et al., 2007]. Another child in the same study developed a simple imitative game between itself and the robot as well as triadic interactions between itself, its caregiver, and the robot after five months of no interest in Keepon. Another child became possessive, gentle, and interactive with Keepon after being initially violent with it, which their therapist said was their typical behaviour when encountering someone new to whom they did not how to relate. The researchers believe that this suggested that the children gradually came to sense a “mind” behind Keepon’s simple attentiveness and emotional responses, which would refute a commonly-held conception of children with autism [Kozima et al., 2009].

Matari´c’s robots

Feil Seifer and Matari´c conducted a pilot study in which a mobile robot equipped with a bubble-gun and large buttons interacted with children with autism, and they found that the children displayed more social actions such as speaking, interacting with the robot, and pressing its buttons when the robot’s behaviour was contingent on the child’s than when the robot’s behaviour was random [Feil-Seifer and Matari´c, 2008a]. When the robot operated in its “contingent” behaviour, it used a control architecture which allowed the robot to engage children with autism in DIR (De- velopmental, Individual-difference, Relationship-based) /Floortime therapy. In this architecture known as B3IA, the robot’s behaviour at any given time was determiend

not only by its immediate sensor data, but also by user-specified preferences, the robot’s interaction history with each child, and an automatic evaluation of the qual- ity of its recent interactions with each child [Feil-Seifer and Matari´c, 2008b]. In an ongoing longitudinal study, Feil Seifer and Matari´c also found that when Bandit, a humanoid robot torso mounted on a mobile base, engaged in DIR/Floortime therapy with children with autism, an automatic classifier applying Gaussian Mixed Models to overhead-camera images of the interactions between the children and the robot was able to correctly group the children’s behaviours 91.4% of the time [Feil-Seifer and Matari´c, 2011].

Robots for studying joint attention

Fasel et al suggested a study that would use computer graphics and robotic systems to study the development of joint attention in infants with and without autism [Fasel et al., 2002]. In a similar vein, Scassellati proposed using a system to help diagnose children with autism by observing measurable behaviours such as gaze direction and focus of attention, position tracking, and vocal prosody while the children interacted with a social robot. Furthermore, because social robots can be programmed to consistently perform specific actions and social cues in precise ways, can make their behaviours more or less nuanced over time according to the severity of an autisticc child’s diagnosis, and are naturally engaging for children with autim, Scassellati argued that robots would be ideal tools for diagnosing, treating, and understanding autism [Scassellati, 2005a] [Scassellati, 2005b]. Ravindra, De Silva, Tadano et al also conducted a study along similar lines by using an autonomous humanoid HOAP robot to try to initiate joint attention with children with autism. By tracking the children’s eye movements from the perspective of a camera looking straight up at the child’s face and classifying the children’s points of gaze according to a mixed Gaussian-based cluster method, the researchers showed that the children gradually increased the amount of time spent participating in joint attention with

the robot in each trial [Ravindra et al., 2009].

Other robots used in autism research

Pioggia, Sica, Ferro et al developed an android head called FACE with an array of touch sensors embedded below its “skin” which is capable of making basic facial expressions, opening and closing its mouth, and moving its eyes. In a preliminary set of trials with four children with autism, the researchers noted that the children spontaneously began to mimic FACE’s head motions and facial expressions during their interactions and also followed its eye gaze after being prompted by their carer [Pioggia et al., 2007]. Later, Liu, Conn, Sarkar et al developed a robotic basketball hoop (a basketball hoop mounted on a 5-degree-of-freedom robot arm) which al- tered its position, movement speed/patterns, and background music in real time to make an child with autism like it, be engaged with it, and have as little anxiety as possible while playing with it. Each child’s support vector machine-based affective model had to be determined beforehand by having the children engage in tasks of concentration under specific, changing circumstances and measuring physiological data (e.g. cardiac activity, skin temperature, electromyograhic activity). After de- veloping models for each child, the researchers found that by reading the children’s physiological data in real time and comparing it with each child’s model, the robotic basketball hoop successfully chose behaviours that promoted engagement, reduced anxiety, and were liked by the children slightly over 75% of the time. Such a system could be used to have a robot socially interact with children with autism and record the child’s physiological reactions to specific behaviours as well as adapt the robot’s behaviour to make the children like it, be more engaged, and not feel anxious [Liu et al., 2008].

Michaud and Th´eberge-Turmel studied many small robotic designs (an ele- phant, a spherical robotic ‘ball’, etc) to see which one best engaged children with autism in playful interactions that helped them develop social skills [Michaud and

Th´eberge-Turmel, 2002]. Later, Duquette, Michaud, et al’s preliminary results showed that for a pair of children with autism, a simple humanoid robot elicited more shared attention and imitations of facial expressions than a human was able to, while a human was able to elicit more imitation of kinesic movements and familiar actions than a humanoid robot [Duquette et al., 2008].