Stanford Seminar - The Next Generation of Robot Learning

For robots to be successful in unconstrained environments, they must be able to perform tasks in a wide variety of situations — they must be able to generalize. We’ve seen impressive results from machine learning systems that generalize to broad real-world datasets for a range of problems. Hence, machine learning provides a powerful tool for robots to do the same. However, in sharp contrast, machine learning methods for robotics often generalize narrowly within a single laboratory environment. Why the mismatch? In this talk, I’ll discuss the challenges that face robots, in contrast to standard machine learning problem settings, and how we can rethink both our robot learning algorithms and our data sources in a way that enables robots to generalize broadly across tasks, across environments, and even across robot platforms.