Machine Learning for Robotic Manipulation
This survey provides an overview of current trends in machine learning for robotic manipulation, which is useful for researchers entering or working within this domain.
This paper surveys recent robotics conferences to identify major trends in applying machine learning techniques to robotic manipulation challenges. It highlights how the dynamic nature of robotics, particularly manipulation, serves as a test-bed for machine learning beyond the passive supervised learning paradigm.
The past decade has witnessed the tremendous successes of machine learning techniques in the supervised learning paradigm, where there is a clear demarcation between training and testing. In the supervised learning paradigm, learning is inherently passive, seeking to distill human-provided supervision in large-scale datasets into high capacity models. Following these successes, machine learning researchers have looked beyond this paradigm and became interested in tasks that are more dynamic. To them, robotics serve as an excellent test-bed, for the challenges of robotics break many of the assumptions that made supervised learning successful. Out of the many different areas within robotics, robotic manipulation has become a favorite area for researchers to demonstrate new algorithms because of the vast numbers of possible applications and its highly dynamical and complex nature. This document surveys recent robotics conferences and identifies the major trends with which machine learning techniques have been applied to the challenges of robotic manipulation.