Recent studies have revealed that using social robots can accelerate the learning process of several skills in areas where autistic children typically show deficits. However, most early research studies conducted interactions via free play. More recent research has demonstrated that robot-mediated autism therapies focusing on core impairments of autism spectrum disorder (e.g., joint attention) yield better results than unstructured interactions. This paper aims to systematically review the most relevant findings concerning the application of social robotics to joint attention tasks, a cardinal feature of autism spectrum disorder that significantly influences the neurodevelopmental trajectory of autistic children. Initially, we define autism spectrum disorder and explore its societal implications. Following this, we examine the need for technological aid and the potentialities of robot-assisted autism therapy. We then define joint attention and highlight its crucial role in children's social and cognitive development. Subsequently, we analyze the importance of structured interactions and the role of selecting the optimal robot for specific tasks. This is followed by a comparative analysis of the works reviewed earlier, presenting an in-depth examination of two distinct formal models employed to design the prompts and reward system that enables the robot to adapt to children's responses. These models are critically compared to highlight their strengths and limitations. Next, we introduce a novel algorithm to address the identified limitations, integrating interactive environmental factors and a more sophisticated prompting and reward system. Finally, we propose further research directions, discuss the most relevant open questions, and draw conclusions regarding the effectiveness of social robotics in the medical treatment of autism spectrum disorders.

翻译：近期研究表明，使用社交机器人能够加速自闭症儿童在多个典型缺陷领域技能的学习过程。然而，早期研究大多通过自由游戏进行互动。最新研究证明，针对自闭症谱系障碍核心缺陷（如联合注意）的机器人介导疗法比非结构化互动取得更好效果。本文旨在系统综述社交机器人在联合注意任务应用方面的关键发现，联合注意作为自闭症谱系障碍的核心特征，显著影响自闭症儿童的神经发育轨迹。首先，我们界定自闭症谱系障碍并探讨其社会影响。随后，我们审视技术辅助的必要性及机器人辅助自闭症治疗的潜力。接着，我们定义联合注意并强调其在儿童社会与认知发展中的关键作用。继而，我们分析结构化互动的重要性以及针对特定任务选择最优机器人的作用。在此基础上，我们对前述研究进行对比分析，深入探讨两种用于设计提示与奖励系统的形式化模型，这些系统使机器人能够适应儿童的反应。通过批判性比较突出这些模型的优势与局限。随后，我们提出一种新型算法以解决已识别的局限，该算法整合了交互环境因素及更精细的提示与奖励系统。最后，我们提出进一步研究方向，讨论最相关的开放性问题，并就社交机器人在自闭症谱系障碍医疗干预中的有效性得出结论。