  Children with Autism Spectrum Disorder find robots easier to communicate with
than humans. Thus, robots have been introduced in autism therapies. However,
due to the environmental complexity, the used robots often have to be
controlled manually. This is a significant drawback of such systems and it is
required to make them more autonomous. In particular, the robot should
interpret the child's state and continuously adapt its actions according to the
behaviour of the child under therapy. This survey elaborates on different forms
of personalized robot behaviour models. Various approaches from the field of
Human-Robot Interaction, as well as Child-Robot Interaction, are discussed. The
aim is to compare them in terms of their deficits, feasibility in real
scenarios, and potential usability for autism-specific Robot-Assisted Therapy.
The general challenge for algorithms based on which the robot learns proper
interaction strategies during therapeutic games is to increase the robot's
autonomy, thereby providing a basis for a robot's decision-making.
