用AI助手帮设计师更高效地设计机器人对话。
Reframing Conversational Design in HRI: Deliberate Design with AI Scaffolds
- 用大模型生成对话提示，解决设计初期无从下手的问题。
- 通过用户反馈精准优化对话提示，提升交互质量。
- 适合需要高质量人机对话的机器人研发团队使用。
大型语言模型（LLMs）使对话机器人得以突破固定对话模式，实现自由交互。然而，缺乏情境适配性的通用输出常导致效果不佳或不恰当。当前设计多依赖直观试错，缺乏系统方法与工具，效率低且不一致。为此，我们提出AI辅助对话引擎（ACE），支持有意识的人机对话设计。ACE包含三项创新：1）基于大模型的语音代理，协助生成初始提示，克服“空白页难题”；2）标注界面，用于收集对话记录的细粒度、具象化反馈；3）利用大模型将用户反馈转化为提示优化。通过两项用户研究验证，结果表明ACE能生成更清晰、具体的机器人行为提示，且基于其生成的提示可带来更高品质的人机对话体验。
原文摘要 · Abstract (English)
Large language models (LLMs) have enabled conversational robots to move beyond constrained dialogue toward free-form interaction. However, without context-specific adaptation, generic LLM outputs can be ineffective or inappropriate. This adaptation is often attempted through prompt engineering, which is non-intuitive and tedious. Moreover, predominant design practice in HRI relies on impression-based, trial-and-error refinement without structured methods or tools, making the process inefficient and inconsistent. To address this, we present the AI-Aided Conversation Engine (ACE), a system that supports the deliberate design of human-robot conversations. ACE contributes three key innovations: 1) an LLM-powered voice agent that scaffolds initial prompt creation to overcome the "blank page problem," 2) an annotation interface that enables the collection of granular and grounded feedback on conversational transcripts, and 3) using LLMs to translate user feedback into prompt refinements. We evaluated ACE through two user studies, examining both designs' experience and end users' interactions with robots designed using ACE. Results show that ACE facilitates the creation of robot behavior prompts with greater clarity and specificity, and that the prompts generated with ACE lead to higher-quality human-robot conversational interactions.
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