Abstract: Most robot learning approaches are focused on discrete, e.g. single-task, learning events. A policy is trained for specific environments and/or tasks, and then tested on similar problems. Yet, in order to be truly autonomous, robots need to be able to react to unexpected events and then update their models/policies to include the just encountered data points. In short, true autonomy requires continual learning. However, continuously updating models without forgetting previously learned mappings remains an open research problem. In this talk I will present learning algorithms, based on localized inference schemes that alleviate the problem of forgetting when learning incrementally. Finally, I will introduce our recent advances on learning-to-learn in the context of continuous learning. We show that, with the help of our meta-learner we achieve faster model adaptation when encountering new situations when learning online.
Biosketch: Franziska Meier is a research scientist at the Max-Planck Institute for Intelligent Systems since August 2016. She is also a postdoctoral researcher with Dieter Fox at the University of Washington, Seattle, since February 2017. Before that she was a PhD student at the University of Southern California. 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 and attended the Georgia Institute of Technology as graduate student in Computer Science. Her research focuses on machine learning for robotics, with a special emphasis on continuous learning for robotics.
Date(s) - Nov 27, 2017
1:30 pm - 2:30 pm