  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.
