Robots That Ask For Help: Uncertainty Alignment for Large Language Model
  Planners
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, which is 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.
Website: https://robot-help.github.io
64a628d185638d0e698ca8f8
Allen Z. Ren
admin_assigned
2023-07-06T14:03:52.809Z
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/avatars/c1e59fbf2252e332a3c86c2bd18afd09.svg
Allen Z. Ren
user
allenzren
64a628d185638d0e698ca8f9
Anushri Dixit
64a628d185638d0e698ca8fa
Alexandra Bodrova
64a628d185638d0e698ca8fb
Sumeet Singh
64a628d185638d0e698ca8fc
Stephen Tu
64a628d185638d0e698ca8fd
Noah Brown
64a628d185638d0e698ca8fe
Peng Xu
64a628d185638d0e698ca8ff
Leila Takayama
64a628d185638d0e698ca900
Fei Xia
64a628d185638d0e698ca901
Jake Varley
64a628d185638d0e698ca902
Zhenjia Xu
64a628d185638d0e698ca903
Dorsa Sadigh
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Andy Zeng
admin_assigned
2023-07-06T14:11:06.397Z
62d5f9d705461e9459d20b91
/avatars/120917da2d8587674505163fb960e27d.svg
Andy Zeng
user
andyzeng
64a628d185638d0e698ca905
Anirudha Majumdar
Robots That Ask For Help: Uncertainty Alignment for Large Language Model
  Planners
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, which is 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.
Website: https://robot-help.github.io
KnowNo is a framework using conformal prediction to measure and align the uncertainty of LLM-based planners, improving efficiency and autonomy in multi-step planning for robots.
conformal prediction LLM-based planners uncertainty modeling robustness multi-step planning simulated setups real robot setups statistical guarantees human preferences Winograd schemas efficiency autonomy model-finetuning foundation models