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Bayesian Active Learning for Intent Disambiguation in Interactive Robot Planning
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
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