Lifelong Learning for Robotics
Current robot learning approaches assume that one can prepare a robot for every possible task and environment variation. With this assumption learning becomes a large data collection effort, followed by a one-time training phase. Once training has converged, learning of the system is terminated and the robot is left to execute it’s skills in the real world, without the ability to further adapt and improve them. Yet, to be truly autonomous, robots need to be able to incrementally and continually acquire new skills, building on top of previously learned representations. In this talk I will present my vision on how to enable life-long learning for robotics.
Franziska Meier is a research scientist at Facebook AI Research. 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.