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 talk, I will 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 generalization 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 show that KnowNo performs favorably over modern baselines 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. 

Bio. Anirudha Majumdar is an Assistant Professor at Princeton University in the Mechanical and Aerospace Engineering (MAE) department and Associated Faculty in the Computer Science department. He also holds a part-time position as a Visiting Research Scientist at the Google AI Lab in Princeton. His group works on controlling highly agile robotic systems in a manner that allows us to make formal guarantees on their safety and performance. 

Majumdar received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. He is a recipient of the Sloan Fellowship, ONR YIP award, the NSF CAREER award, the Google Faculty Research Award (twice), the Amazon Research Award (twice), the Young Faculty Researcher Award from the Toyota Research Institute, the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA), the Paper of the Year Award from the International Journal of Robotics Research (IJRR), the Alfred Rheinstein Faculty Award (Princeton University), and the Excellence in Teaching Award from Princeton's School of Engineering and Applied Science.