Towards robots that generalize and adapt efficiently
Date: February 28, 2025 @ 3:00-4:00PM | Location: Gates B03 | Speaker: Franziska Meier | Affiliation: Meta
Abstract:
While there has been major investment in developing large-scale robot learning algorithms, achieving true autonomy remains a wide-open research question. A key ingredient towards this goal is a robots ability to generalize to unseen scenarios well enough such that it can bootstrap learning and adaptation efficiently. In this talk, I’ll present examples of FAIR robotics research towards the goal of learning general representations for a wide spectrum of robotics applications.
Bio:
Franziska Meier is a research scientist at FAIR. Previously she was a research scientist at the Max-Planck Institute for Intelligent Systems and a postdoctoral researcher with Dieter Fox at the University of Washington, Seattle. She received her PhD from the University of Southern California, where she defended her thesis on “Probabilistic Machine Learning for Robotics” in 2016, under the supervision of Prof. Stefan Schaal. Prior to her PhD studies, she received her Diploma in Computer Science from the Technical University of Munich. Her research focuses on machine learning for robotics, with a special emphasis on lifelong learning for robotics.