Using Robots in Autism Therapy: A Survey of Ongoing Research Marjorie Skubic Associate Professor Electrical and Computer Engineering Dept. Computer Science Dept. (joint apt.)

Outline Motivation – How I got interested Autistic disorders A survey of the research – Why robots might help The field of researchers Conclusions

How I got interested Research in Human-Robot Interaction Looking for a killer application Better – How can we use robots to help people? Talks at the IEEE RO-MAN 2005 Workshop

Autistic Disorders 1 of 300 children diagnosed with autism with rates rising – 1 of 800 children diagnosed with Down syndrome – 1 of 450 children diagnosed with juvenile diabetes – 1 of 333 children will develop cancer by age 20 Diagnosis currently made through behavioral observation – No blood test or genetic screening is available although there is evidence of a genetic link

Autistic Disorders: Characteristics Inability to relate to other people Little use of eye contact with other people Difficulty understanding gestures and facial expressions Difficulties with verbal & non-verbal communication Difficulty understanding other’s intentions, feelings, and mental states

Why Use Robots? Most children, including children with autism, are attracted to robots. This natural affinity is exploited, and the robot is used as an interactive toy. Robots may provide a less threatening environment than interacting with people. – Robots can provide a repetitive and more predictable environment. – This “safe” environment can gently push a child with autism towards human interaction.

The Connection to Imitation One theory: Autism may be caused by early impairments in imitation and shared attention (Rogers & Pennington, 1991) (Baron-Cohen, 1995) Imitation is a format of communication, a means to express interest and engage others in interaction (Nadel, 1999) Idea: Use a doll-like robot to engage children with autism and teach basic imitative interaction skills – From: K. Dautenhahn, and A. Billard, Games Children with Autism Can Play With Robota, a Humanoid Robotic Doll, Proc. 1st Cambridge Workshop on Universal Access and Assistive Technology, 2002

Robota A six-year old autistic boy playing with Robota. He seemed curious about Robota's head movements and so he touches the doll. From: K. Dautenhahn, and A. Billard, Games Children with Autism Can Play With Robota, a Humanoid Robotic Doll, Proc. 1st Cambridge Workshop on Universal Access and Assistive Technology, 2002

“Robota … allows the child to understand that the doll’s movement originates from his own movement (sense of agency) and is limited to a restricted category of movement (enhances intentional action)” From: J. Nadel, “Early Imitation and a Sense of Agency,” Proc. 4 th Intl. Workshop on Epigenetic Robots, 2004 Imitation Using Robota

An autistic child playing “chasing” games with the mobile robot From: K. Dautenhahn, and A. Billard, Games Children with Autism Can Play With Robota, a Humanoid Robotic Doll, Proc. 1st Cambridge Workshop on Universal Access and Assistive Technology, 2002

Joint Attention Using Robota From: B. Robins, P. Dickerson, and K. Dautenhahn, “Robots as Embodied Beings – Interactionally Sensitive Body Movements In Interactions Among Autistic Children and a Robot,” Proc. RO-MAN 2005 Robota is controlled via teleoperation by the investigator.

The investigator encourages the children to show each other how they can interact with the robot. The robot will not move unless the children show the same movement, i.e., they must work together. Two autistic children: Note Andy’s gaze at Jack.

Andy and Jack touch each other to balance themselves while each raising a leg.

Adam shows no interest in his classmates and usually tries to avoid the rest of the children. But Adam is interested in Robota. Adam takes Rob’s hand to show him how to interact with Robota.

Interacting with Keepon From: H. Kozima, C. Nakagawa, and Y. Yasuda, “Interactive Robots for Communication-Care: A Case Study in Autism Therapy,” Proc. RO-MAN 2005 Keepon is controlled via teleoperation.

Views from Keepon’s camera eyes

Attentive action Emotive action Keepon's kinematic mechanism. Two gimbals are connected by four wires; the lower gimbal is driven by two motors. Another motor rotates the whole inner-structure; yet another drives the skull downward for bobbing.

Enabling Interaction Joint attention: Sharing the perceptual information Eye-contact: Referring to each other's mental states Enables people to exchange intention and emotion toward a target.

Emergence of dyadic interaction. Spontaneous actions to Keepon (left) and actions copied from others (right). Emergence of triadic interaction. The child discovers excitement in Keepon (left) and then looks at the adult to share the excitement (right).

Using Robots for Autism Diagnosis From: B. Scassellati, “Quantitative Metrics of Social Response for Autism Diagnosis,” Proc. RO-MAN 2005 ESRAPlaytest

Autism Diagnosis Methods Reaction to the ESRA robot with and without the face configuration Can generate facial expressions using 5 servo motors

Autism Diagnosis Methods Measure listening preferences to speech sounds At the press of a button, an audio clip is played. The interaction is logged in non-volatile memory.

Autism Diagnosis Methods Vocal prosody, i.e., how something is said Separation of two features used in a Bayesian classifier distinguishes low energy categories (neutral and soothing) from high energy categories (approval, attention, and prohibition). Features F24 vs. F1 Mean pitch * energy vs. mean pitch

Autism Diagnosis Methods Position tracking relative to another person

Autism Diagnosis Methods Gaze direction and focus of attention Red – adolescents with autism Blue – typical adolescents

For example, F(au)*G(self) indicates a filter trained on an individual with autism and tested on that same individual while F(nc)*G(au) indicates a filter trained on a control individual and tested on an individual with autism. The mean performance of this data (y-axis) is a function of the response percentile of individual pairings. Significant differences (all p<0.01 for a two-tailed t-test) are seen between the following classes: (1) F(nc)*G(self), (2) F(au)*G(self), (3) F(nc)* G(other nc), and (4) the three other conditions. Linear discriminant analysis of autistic (au) and typical (nc) gaze patterns. Linear filters F(x) are trained to reproduce the gaze pattern G(x) of each individual x and then applied to predict the gaze patterns of any other individual.

University of Sherbrooke Project for engineering students: – Design a robotic toy for an autistic child Educational value – Real world problem – Students work together in a team – Students must first investigate autistic disorders

University of Sherbrooke Pushing Jumbo around the play area. Rolling game with Roball. From: Michaud, F., Théberge-Turmel, C. (2002), "Mobile robotic toys and autism", Socially Intelligent Agents - Creating Relationships with Computers and Robots, Kluwer, pp

University of Sherbrooke Assembling the arms and tail of C-Pac. Girl showing signs of interest toward Bobus. From: Michaud, F., Théberge-Turmel, C. (2002), "Mobile robotic toys and autism", Socially Intelligent Agents - Creating Relationships with Computers and Robots, Kluwer, pp

The Field of Researchers Francois Michaud – University of Sherbrooke, Canada Kerstin Dautenhahn & Ben Robbins – University of Hertfordshire, UK Aude Billard – Swiss Federal Institute of Technology (EPFL) Jacqueline Nadel – French National Centre of Scientific Research

The Field of Researchers Brian Scassellati and Bob Schultz – Yale University Javier Movellan – University of California – San Diego Hideki Kozima – National Institute of ICT, Japan Michio Okada – ATR, Kyoto, Japan

Conclusions The use of robots for autism therapy and diagnosis is just beginning. There is anecdotal evidence that robot therapy can help children with autism How can we start here at MU with the new Thompson Family Center for Autism and Neurodevelopmental Disorders?

Maybe the Tiger Kitty The iCat by Philips Research

Acknowledgements Thanks to Brian Scassellati, Francois Michaud, Ben Robins, and Hideki Kozima for helpful discussions.