不確実性下での月面ローバー軌道計画に対する大規模言語モデルによる説明支援
Large Language Model Explainability for Planning Lunar Rover Trajectories under Uncertainty
月面ローバーの軌道計画を科学者が検証できるよう、計画結果を専門用語で説明し条件を変えて再実行できるLLMベースの対話型アシスタントを提案し、応答の90.9%が計画出力に忠実であることを示した。
著者: Michelle Ho, Maximilian Adang, Zachary Booth, David E. Astle, Margaret Hansen, Edward Balaban, Mykel J. Kochenderfer
分類: cs.RO
原文アブストラクト
Planning planetary rover trajectories under probabilistic uncertainty is a multi-objective optimization problem that science teams currently iteratively solve and verify by hand. This process becomes impractical when timelines and constraints frequently shift throughout the mission planning phase. Sampling-based planners such as Monte Carlo tree search can speed up this process, but their outputs do not preserve traceability to the science objectives that justify each plan, which the science team needs to verify. We propose an interactive assistant based on a large language model (LLM) that can explain planner trajectories using domain vocabulary and re-run the planner under user-specified conditions to compare alternatives. Previous work has used LLMs inside planner decision-making loops, but we explore using them for post-planning explanation, where they are more likely to be trusted since they do not directly influence planner decisions. Our framework pairs a simulator inspired by the NASA Volatiles Investigating Polar Exploration Rover (VIPER) mission with a ReAct queryable interface that calls curated action tools and retrieval functions. We evaluate the assistant on a query bank consisting of plan comparisons, feasibility, and lookup queries. Experiments show that 90.9% of the assistant's responses are faithful to the planner's outputs, a nearly 65% improvement over zero-shot prompting of the same LLM without tool access, demonstrating the potential for trustworthy explainability of uncertainty-aware traverses with an LLM-based system.