The robotic intervention for individuals with Autism Spectrum Disorder (ASD) has generally used pre-defined scripts to deliver verbal content during one-to-one therapy sessions. This practice restricts the use of robots to limited, pre-mediated instructional curricula. In this paper, we increase robot autonomy in one such robotic intervention for children with ASD by implementing perspective-taking teaching. Our approach uses large language models (LLM) to generate verbal content as texts and then deliver it to the child via robotic speech. In the proposed pipeline, we teach perspective-taking through which our robot takes up three roles: initiator, prompter, and reinforcer. We adopted the GPT-2 + BART pipelines to generate social situations, ask questions (as initiator), and give options (as prompter) when required. The robot encourages the child by giving positive reinforcement for correct answers (as a reinforcer). In addition to our technical contribution, we conducted ten-minute sessions with domain experts simulating an actual perspective teaching session, with the researcher acting as a child participant. These sessions validated our robotic intervention pipeline through surveys, including those from NASA TLX and GodSpeed. We used BERTScore to compare our GPT-2 + BART pipeline with an all GPT-2 and found the performance of the former to be better. Based on the responses by the domain experts, the robot session demonstrated higher performance with no additional increase in mental or physical demand, temporal demand, effort, or frustration compared to a no-robot session. We also concluded that the domain experts perceived the robot as ideally safe, likable, and reliable.

翻译：针对自闭症谱系障碍个体的机器人干预通常采用预定义脚本在一对一治疗会话中提供语言内容。这种做法将机器人的使用限制在有限的、预先编排的教学课程中。本文通过实施观点采择教学，提高了针对ASD儿童的此类机器人干预的自主性。我们的方法使用大型语言模型生成文本形式的语言内容，然后通过机器人语音传递给儿童。在所提出的流程中，我们通过观点采择教学使机器人承担三种角色：发起者、提示者和强化者。我们采用GPT-2 + BART流程来生成社交情境、提出疑问（作为发起者），并在需要时提供选项（作为提示者）。机器人通过为正确答案提供积极强化来鼓励儿童（作为强化者）。除了技术贡献外，我们与领域专家进行了十分钟的模拟实际观点教学会话，研究人员扮演儿童参与者。这些会话通过包括NASA TLX和GodSpeed在内的问卷调查验证了我们的机器人干预流程。我们使用BERTScore比较了GPT-2 + BART流程与纯GPT-2流程，发现前者性能更优。根据领域专家的反馈，与无机器人会话相比，机器人会话表现出更高的性能，且未额外增加心理或生理需求、时间需求、努力程度或挫败感。我们还得出结论，领域专家认为该机器人在安全性、亲和力和可靠性方面均表现理想。