[meetings] Talking Robotics #51 - Taylor Kessler Faulkner, University of Washington

[Talking Robotics]

(apologies for cross-posting)

Greetings,

We're excited to have Taylor Kessler Faulkner for Talking Robotics #51 next week. Registration is free (and mandatory). The talk and discussion will be recorded and shared publicly.

Session Info:
Title: Enabling Robots to Adapt to and Learn from Real People
Schedule: 25th January - 9am PST | 5pm GMT | 6pm CET
Registration: https://us02web.zoom.us/meeting/register/tZclcOivqj4tG9wR6SviPsMc4XNGf_UBET1f

Abstract:
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 lens of working with real teachers and possibly imperfect information.

Speaker Bio:
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.

Speaker Links:

Website, Google Scholar, LinkedIn

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Talking Robotics

Silvia Tulli, Kush Desai, Riddhiman Laha, Sooraj Krishna, Nitin Ragothaman