Today, robot learning paradigms rely on human-provided data, (e.g. demonstrations, preference labels) to adapt their behavior and align with user intent. Yet in practice, this process of teaching robots is one of trial-and-error that places the burden on humans to decipher what the robot misunderstands, diagnose failures, and supply the “right” corrective data.
My research develops user-centric active learning methods that learn by supporting human teachers.
Michelle Zhao,
Carnegie Mellon University
In this talk, I will first introduce uncertainty quantification tooling that extends conformal prediction to the human-robot interaction setting, enabling robots to rigorously “know when they don’t know” even when relying on black-box policies. I will then discuss how these uncertainty self-assessments enable robots to communicate insights with human teachers and proactively ask for targeted feedback within novel interactive learning paradigms. Coupling these ideas with cost-optimal planning algorithms, I will demonstrate how robots can interleave both learning and collaboration with human partners over multitask sequences. I will end this talk by taking a step back and examining the alignment process for robotics and discussing opportunities for how rethinking interactive learning as collaborative and continual accounts for not only task, but the nuanced interaction dynamics present during the teaching process.
Michelle Zhao is a Ph.D. candidate at Carnegie Mellon University in the Robotics Institute, working with Professors Henny Admoni and Reid Simmons. She studies human-robot interaction, with an emphasis on how robots can learn from and about people. Her research integrates methods from statistical uncertainty quantification, machine learning, and human-robot interaction to develop theoretical frameworks and practical algorithms for active learning from human feedback in domains like assistive robotic manipulation. Prior to her Ph.D., she earned her B.S. at the California Institute of Technology. She is the recipient of the Siebel Scholarship, Rising Stars in Computational and Data Sciences, the NDSEG Research Fellowship, HRI Pioneers 2025 Honorable Mention, and has worked at Toyota Research Institute.