Large language models (LLMs)  exhibit a wide range of promising capabilities — from step-by-step planning to commonsense reasoning — that may provide utility for robots, but remain prone toconfidentlyhallucinated predictions. In this work, we presentKnowNo, which is a framework for measuring and aligning the uncertainty of LLM-based planners such that theyknow when they don’t knowand ask for help when needed.KnowNobuilds on the theory ofconformal predictionto provide statistical guarantees on task completion while minimizing human help in complex multi-step planning settings.
Experiments across a variety of simulated and real robot setups that involve tasks with different modes of ambiguity (e.g., from spatial to numeric uncertainties, from human preferences to Winograd schemas)  show thatKnowNoperforms favorably over modern baselines (which may involve ensembles or extensive prompt tuning)  in terms of improving efficiency and autonomy, while providing formal assurances.KnowNocan be used with LLMs out of the box without model-finetuning, and suggests a promising lightweight approach to modeling uncertainty that can complement and scale with the growing capabilities of foundation models.111Webpage with additional information, videos, and code:https://robot-help.github.io