Abstract
The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to characterise this, and emphasise that future research should tackle this explicitly in realistic settings.
Highlights
Having robots engage socially with humans is a desirable goal for social robotics
Some are due to practical constraints, but the more problematic issues are typically related to diagnostic criteria involving a covert behavioural component, i.e., those behaviours that require some degree of interpretation in addition to the observation of the overt phenomena
We propose this framework as a guideline for clinicians wishing to incorporate technological means of behaviour measurement into the diagnosis of Autism Spectrum Disorder (ASD), as well as for researchers looking to develop and improve such technologies
Summary
Having robots engage socially with humans is a desirable goal for social robotics. It lowers the barrier to entry into interactions, as it allows the humans to engage and interact with the robot in a way similar to how they would interact with another human. Merely predicting the outcome of actions is not the same as understanding internal mental states from observable kinematics The latter is seen as a pre-requisite for truly social robotics, yet remains a challenge [7]. On the other hand, diagnosing ASD does require the ability to observe social interactions and infer underlying mental states, which is the core requirement for social robots that we are interested in here. It is a domain for which clear protocols, assessment criteria and so on exist. We discuss the degree to which technological means can fulfil these requirements
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