Taylor Kessler Faulkner



Taylor Kessler Faulkner is a postdoctoral scholar and UW Data Science Postdoctoral Fellow in Siddhartha Srinivasa’s Personal Robotics Lab at the University of Washington. She graduated from UT Austin in August 2022 with a PhD in Computer Science, where she worked with Prof. Andrea Thomaz in the Socially Intelligent Machines Lab. Taylor’s research focuses on enabling robots to learn and adapt to real people. Her goal is to create algorithms that allow robots to learn from and adapt to potentially inaccurate or inattentive human teachers.

Speakers Links: Website, Google Scholar, Linkedin


Robots deployed in the wild can improve their performance by using input from human teachers. Furthermore, both robots and humans can benefit when robots adapt to and learn from the people around them. However, real people can act in imperfect ways, and can often be unable to provide input in large quantities. In this talk, I will address some of the past research I have conducted towards addressing these issues, which has focused on creating learning algorithms that can learn from imperfect teachers. I will also talk about my current work on the Robot-Assisted Feeding project in the Personal Robotics Lab at UW, which I am approaching through a similar lense of working with real teachers and possibly imperfect information.


  • Faulkner, T.A., Gutierrez, R., Short, E.S., Hoffman, G., & Thomaz, A.L. (2019). Active Attention-Modified Policy Shaping: Socially Interactive Agents Track. Adaptive Agents and Multi-Agent Systems link.
  • T. K. Faulkner, E. S. Short and A. L. Thomaz, “Policy Shaping with Supervisory Attention Driven Exploration,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 842-847, doi: 10.1109/IROS.2018.8594312 link.
  • Taylor A. Kessler Faulkner and Andrea Thomaz. 2021. Interactive Reinforcement Learning from Imperfect Teachers. In Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (HRI ‘21 Companion). Association for Computing Machinery, New York, NY, USA, 577–579. https://doi.org/10.1145/3434074.3446361 link.