Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity

Kaiqu Liang, Zixu Zhang, Jaime Fernández Fisac

Keywords: Large Language Models, Conformal Prediction, Uncertainty Quantification, Foundation Models for Decision Making

TL;DR: This paper propose introspective planning to guide Large Language Models (LLMs) planning with uncertainty awareness, and achieve a tighter confidence bound with conformal prediction.

Abstract:

Large language models (LLMs) exhibit advanced reasoning skills, enabling robots to comprehend natural language instructions and strategically plan high-level actions through proper grounding. However, LLM hallucination may result in robots confidently executing plans that are misaligned with user goals or even unsafe in critical scenarios. Additionally, inherent ambiguity in natural language instructions can introduce uncertainty into the LLM's reasoning and planning. We propose introspective planning, a systematic approach that guides LLMs to refine their own uncertainty in alignment with inherent task ambiguity. Our approach constructs a knowledge base containing introspective reasoning examples as post-hoc rationalizations of human-selected safe and compliant plans, which are retrieved during deployment. Evaluations on three tasks, including a new safe mobile manipulation benchmark, indicate that introspection substantially improves both compliance and safety over state-of-the-art LLM-based planning methods. Additionally, we empirically show that introspective planning, in combination with conformal prediction, achieves tighter confidence bounds, maintaining statistical success guarantees while minimizing unnecessary user clarification requests.

Primary Area: Natural language processing

Submission Number: 2620