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.
翻译：患自闭症的自闭症儿童比人类更容易与自闭症儿童沟通。 因此,机器人被引入自闭症疗法。 但是,由于环境的复杂性,使用过的机器人往往需要人工控制。 这是这些系统的一大缺陷,需要使他们更加自主。 特别是,机器人应该解释儿童的状况,并根据治疗中儿童的行为不断调整其行动。 本调查详细介绍了不同形式的个性化机器人行为模式。 讨论了人类-机器人互动领域以及儿童-机器人互动领域的各种办法。 目的是比较这些办法的缺陷、真实情景中的可行性以及自闭症特定机器人辅助疗法的潜在可用性。 机器人在治疗游戏中学习适当互动策略所依据的算法的一般挑战是提高机器人的自主性,从而为机器人的决策提供基础。
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