ロボ・ブロック:ソーシャルロボットのエンドユーザー設計・プログラミングにおける生成的足場かけ
Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots
LLMを活用したブロック型プログラミング環境「Robo-Blocks」を設計し、初心者が物語構造を通じてロボットの行動を生成できるようにする手法を提案・評価した。
詳しい要約
1. どんなもの?
2. 先行研究と比べてどこがすごい?
3. 技術・手法の肝は?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Arissa J. Sato, Callie Y. Kim, Nathan Thomas White, Abhinav Maneesh, Yuqing Wang, Hui-Ru Ho, Bilge Mutlu
分類: cs.HC, cs.RO
原文アブストラクト
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.
