Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners

Allen Z. Ren, Anushri Dixit, Alexandra Bodrova, Sumeet Singh, Stephen Tu, Noah Brown, Peng Xu, Leila Takayama, Fei Xia, Jake Varley, Zhenjia Xu, Dorsa Sadigh, Andy Zeng, Anirudha Majumdar

Research output: Contribution to journalConference articlepeer-review


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 to confidently hallucinated predictions. In this work, we present KNOWNO, a framework for measuring and aligning the uncertainty of LLM-based planners, such that they know when they don't know, and ask for help when needed. KNOWNO builds on the theory of conformal prediction to 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 that KNOWNO performs favorably over modern baselines (which may involve ensembles or extensive prompt tuning) in terms of improving efficiency and autonomy, while providing formal assurances. KNOWNO can 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.

Original languageEnglish (US)
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event7th Conference on Robot Learning, CoRL 2023 - Atlanta, United States
Duration: Nov 6 2023Nov 9 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


  • conformal prediction
  • Language-based planning
  • uncertainty estimation


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