How to teach robots interact with children?



People with autism see, hear and feel the world differently from other people, which affects how they interact with others. This makes communication-centred activities quite challenging for children with autism spectrum conditions (ASCs). Therapists therefore find it difficult to engage them in these activities during educational therapy.

To address this challenge, therapists recently began to use humanoid robots in therapy sessions. However, existing robots lack the ability to autonomously engage with children, which is vital for improving the therapy. And the fact that people with ASCs have atypical and diverse styles of expressing their thoughts and feelings makes the use of such robots even more challenging.

Researchers working on the EU-funded project EngageME have now created a personalised machine learning framework for robots used during autism therapy. As they describe in their paper published in Science Robotics, this framework helps robots automatically perceive the affect – facial, vocal and gestural behaviour – and engagement of children as they interact with them.

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A personalised approach

To achieve this exciting advance, project partners had realised that in the case of children with ASCs, one size doesn't fit all. As a result, they personalised their framework to each child using demographic data, behavioural assessment scores and other characteristics unique to that child. The novel framework enabled the robots to automatically adapt their interpretations of children's responses by taking into account cultural and individual differences between them.

"The challenge of creating machine learning and AI [artificial intelligence] that works in autism is particularly vexing, because the usual AI methods require a lot of data that are similar for each category that is learned. In autism where heterogeneity reigns, the normal AI approaches fail," explained co-author Prof. Rosalind Picard in an article posted on "MIT News."

Robot-assisted therapy

The researchers tested their model on 35 children from Japan and Serbia. Aged 3 to 13, the children interacted with the robots in 35-minute sessions. The humanoid robots conveyed different emotions – anger, fear, happiness and sadness – by changing the colour of their eyes, the tone of their voice and the position of their limbs.

As it interacted with a child, the robot would capture video of their facial expressions, movements and head pose, as well as audio recordings of their tone of voice and vocalisations. A monitor on each child's wrist also provided the robot with data on their body temperature, heart rate and skin sweat response. The data was used to extract the child's various behavioural cues and was then fed into the robot's perception module.

Using deep learning models, the robot then estimated the child's affect and engagement based on the extracted behavioural cues. The results were used to modulate the child-robot interaction in subsequent therapy sessions.

Audiovisual recordings of the therapy sessions were also observed by human experts. Their assessments of the children's responses showed a 60 % correlation with the robots' perceptions. This was a higher agreement level than achieved between human experts. The study's results suggest that trained robots could play an important role in autism therapy in the future.


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EngageME (Automated Measurement of Engagement Level of Children with Autism Spectrum Conditions during Human-robot Interaction) is working to augment robots with key information that will help therapists personalise therapies and make human-robot interaction more engaging and natural.

Pushing limits

For Koop, mass-market robots like Vector offer a new way to explore her research into one of the most challenging problems in the field—how to make AI systems transfer their "knowledge" to tasks beyond the specific problems they're designed to solve.

"If a human expert can do it, AI probably can," she explained. "But if a baby or puppy can do it, AI probably can't."
Our biological brains' ability to adapt what we learn to new situations also raises big questions about what humans might be able to learn from smart robots that act a lot like us, said Koop.

She noted the Anki designers spent a lot of time making the expressions rendered in Vector's big blue digital eyes seem like genuine emotions—even bringing a former Pixar animator on board as "character director."

"Having a robot do eye contact is hard, even though it's not real eyes," she said. "But it does have cameras, and the eyes are animated to express something that is going on internally. That is the correct direction to go for robotics."

If all that effort means a child comes to regard their little robot friend as something that can feel fear or pain if it's mistreated, so much the better, Koop said.

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"If they interact with these toys in an empathetic way, that's got to be a good thing. Whether it's an imaginary friend, a live pet or a robot, you're reinforcing habits. Practising skills of whatever variety improves them."

Koop is anxious to get her hands on yet-to-be-released developer tools for Vector so she can get inside its head and see just what—and how—it's learning. In the meantime, she said, the most immediate benefit kids stand to gain from AI toys is learning the basics of skills that will only get more important in an AI-powered world.

"It's a fun platform for coding. A key skill in programming and computing science is the ability to break down a problem into steps. You're not going to get super-sophisticated programming from the drag-and-drop interface (in Cozmo's app), but it's a nice, tangible way to see what's happening."

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