  Several robot manipulation tasks are extremely sensitive to variations of the
physical properties of the manipulated objects. One such task is manipulating
objects by using gravity or arm accelerations, increasing the importance of
mass, center of mass, and friction information. We present SwingBot, a robot
that is able to learn the physical features of a held object through tactile
exploration. Two exploration actions (tilting and shaking) provide the tactile
information used to create a physical feature embedding space. With this
embedding, SwingBot is able to predict the swing angle achieved by a robot
performing dynamic swing-up manipulations on a previously unseen object. Using
these predictions, it is able to search for the optimal control parameters for
a desired swing-up angle. We show that with the learned physical features our
end-to-end self-supervised learning pipeline is able to substantially improve
the accuracy of swinging up unseen objects. We also show that objects with
similar dynamics are closer to each other on the embedding space and that the
embedding can be disentangled into values of specific physical properties.
