让机器人更懂人话，自动提炼用户指令的真正意图
Distill: Uncovering the True Intent behind Human-Robot Communication
- 通过去除冗余步骤、抽象化动作含义、放宽顺序限制来提炼用户指令
- 在众包实验中验证了该方法能有效挖掘并优化初始任务描述中的真实意图
- 适合需要自然语言交互的机器人系统研发者和人机协作场景设计者
随着机器人日益融入日常环境，自然语言和用户自定义编程等直观交互方式已成为指定自主机器人行为的关键手段。然而，这些机制难以完整捕捉用户意图：自然语言模糊不清，而用户编程又可能过于具体。因此，理解用户与机器人交互时的真实意图，仍是人-人工智能系统中的核心挑战。为此，我们提出了用于人机交互界面的Distill方法。给定用户提供的任务说明，Distill（1）移除不必要的步骤；（2）抽象出各步骤背后的通用意义；（3）放宽步骤间的顺序约束。我们在网页界面实现了该方法，并通过众包研究证明其能够从初始任务描述中有效提取并优化用户意图。
原文摘要 · Abstract (English)
As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for human-robot communication interfaces. Given a task specification provided by the user, Distill (1) removes unnecessary steps; (2) generalizes the meaning behind individual steps; and (3) relaxes ordering constraints between steps. We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.
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