交互式机器人规划中意图消歧的贝叶斯主动学习
Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning
- Massachusetts Institute of Technology（麻省理工学院）
- GE Vernova（通用电气维诺瓦）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对交互式机器人规划中指令歧义问题，提出贝叶斯主动学习框架，利用LLM初始化STL规范并优化澄清查询，提升任务满意度并减少澄清轮次。
AI中文摘要：
交互式机器人规划要求机器人从自然语言指令中推断并执行人类意图，而这些指令往往具有歧义、不完整或规定不足的特点。尽管大型语言模型（LLM）为澄清提供了强大的接口，但依赖生成模型驱动多轮对话可能引入系统性失败。我们提出了一种贝叶斯框架，将澄清视为基于接地信号时序逻辑（STL）任务规范的主动学习问题。我们的方法使用LLM来初始化候选形式化规范，并将信息性对比转化为自然语言澄清问题，同时贝叶斯优化维持对用户意图的不确定性估计，并选择最大化信息增益的查询。收敛后，推断出的STL规范被传递给形式化规划器，以合成可验证的机器人轨迹。在四个模拟和真实世界任务领域中，我们的方法通常比LLM基线实现更高的任务满意度，并需要更少的澄清轮次，同时帮助较小的模型缩小与较大推理模型的性能差距。
英文摘要：
Interactive robot planning requires robots to infer and execute human intentions from natural language instructions that are often ambiguous, incomplete, or underspecified. Although large language models (LLMs) provide a powerful interface for clarification, relying on the generative model to drive an multi-turn conversation can introduce systematic failures. We propose a Bayesian framework that treats clarification as an active learning problem over grounded Signal Temporal Logic (STL) task specifications. Our method uses LLMs to initialize candidate formal specifications and translate informative contrasts into natural-language clarification questions, while Bayesian optimization maintains uncertainty estimation over user intent and selects queries that maximize information gain. After convergence, the inferred STL specification is passed to a formal planner to synthesize a verifiable robot trajectory. Across four simulated and real-world task domains, our approach generally achieves higher task satisfaction and requires fewer clarification rounds than LLM baselines, while helping smaller models close the performance gap against larger reasoning models.
