2023-11-28 プリンストン大学
Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know and ask for clarification from a human. Photo by the researchers
◆この手法は高い精度を保ちつつ、他の手法に比べてロボットが必要な助けの量を減らします。ユーザーは成功度目標を設定し、不確実性の閾値に基づいてロボットに助けを求める条件を指定できます。将来的には、この手法は視覚と言語情報を組み合わせたモデルに基づいてアクティブパーセプションの問題にも拡張される予定です。
＜関連情報＞
- https://engineering.princeton.edu/news/2023/11/28/how-do-you-make-robot-smarter-program-it-know-what-it-doesnt-know
- https://openreview.net/forum?id=4ZK8ODNyFXx
助けを求めるロボット：大規模言語モデルプランナーのための不確実性アライメント 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
Conference on Robot Learning Published: 31 Aug 2023
Abstract
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 (for example, 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.
