Problem
Research questions and friction points this paper is trying to address.
Interactive Robot Planning
Intent Disambiguation
Natural Language Instructions
Active Learning
Large Language Models
Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning
🤖 AI Summary
This study addresses the problem of intent inference failures in interactive robot planning caused by ambiguous and incomplete natural language instructions. We propose an active clarification framework that integrates large language models (LLMs) with Bayesian optimization. Specifically, multi-turn dialogue disambiguation is formulated as an active learning process grounded in Signal Temporal Logic (STL). The LLM generates formal specifications and natural language queries, while Bayesian optimization estimates uncertainty and selects questioning strategies that maximize information gain; a formal planner then synthesizes verifiable trajectories. Evaluations across simulated and real-world task domains demonstrate that our approach significantly improves task satisfaction and reduces clarification turns compared to LLM baselines, effectively narrowing the performance gap between smaller and larger language models.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian Active Learning
Intent Disambiguation
Signal Temporal Logic
Large Language Models
Interactive Robot Planning
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