Large Language Model Explainability for Planning Lunar Rover Trajectories under Uncertainty
Overview
This research introduces an LLM-based interactive assistant designed to explain planetary rover trajectory plans generated by sampling-based planners under probabilistic uncertainty. The system pairs a simulator inspired by the NASA VIPER mission with a ReAct queryable interface, allowing users to compare alternatives and verify traceability to science objectives. Experiments on a query bank of plan comparisons, feasibility checks, and lookups show that 90.9% of the assistant's responses are faithful to the planner's outputs, representing a nearly 65% improvement over zero-shot prompting without tool access. The study demonstrates potential for trustworthy explainability in uncertainty-aware traverses but remains within simulation and does not claim operational deployment.
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- arXiv Robotics — research abstractsSignalLarge Language Model Explainability for Planning Lunar Rover Trajectories under Uncertainty
This research introduces an LLM-based interactive assistant designed to explain planetary rover trajectory plans generated by sampling-based planners under probabilistic uncertainty. The system pairs a simulator inspired by the NASA VIPER mission with a ReAct queryable interface, allowing users to compare alternatives and verify traceability to science objectives. Experiments on a query bank of plan comparisons, feasibility checks, and lookups show that 90.9% of the assistant's responses are faithful to the planner's outputs, representing a nearly 65% improvement over zero-shot prompting without tool access. The study demonstrates potential for trustworthy explainability in uncertainty-aware traverses but remains within simulation and does not claim operational deployment.
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