用生成式支架助新手编程社交机器人，避免依赖语言模型
Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
- 用结构化叙事将抽象想法转为可执行机器人行为
- 实测发现用户通过支架形成新设计策略与使用模式
- 适合想提升编程能力的新手或教育场景使用者
新手在编程社交机器人时面临规划、交互设计和编程多重挑战。尽管大语言模型（LLMs）可通过自然语言生成代码展现潜力，但可能掩盖编程关键要素并取代设计意图，导致过度依赖而非技能成长。本文通过设计研究（RtD）方法，提出Robo-Blocks——一种基于积木的编程环境，利用LLMs提供生成式支架，将高层次创意与可执行机器人行为通过结构化叙事相连接。在新手用户中部署测试后，我们识别出新的用户角色与使用模式，揭示生成式支架如何影响终端用户的设计与编程策略。研究提炼出有效使用生成式支架的设计洞察，并探讨其融入社交机器人编程实践的方法。
原文摘要 · Abstract (English)
Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. While large language models (LLMs) hold significant promise through code generation from natural-language descriptions, they can obscure critical elements of programming and supplant designer intent, eventually resulting in over-reliance instead of developing programming skills. In this paper, we explore how LLM-based social-robot-programming tools can support novice robot programmers through a Research through Design (RtD) process. We designed and prototyped Robo-Blocks, a block-based programming environment that leverages LLMs to offer novice robot programmers generative scaffolding through structured narratives that connect high-level ideas to executable robot behaviors. Through deployment with novices, we discovered emerging user personas and usage patterns for generative scaffolding and showed how this scaffolding shapes end-user design and programming strategies. We present design insights for the effective use of generative scaffolding and its integration into the practice of social-robot programming.
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