  This paper introduces a new technique for learning probabilistic models of
mass and friction distributions of unknown objects, and performing robust
sliding actions by using the learned models. The proposed method is executed in
two consecutive phases. In the exploration phase, a table-top object is poked
by a robot from different angles. The observed motions of the object are
compared against simulated motions with various hypothesized mass and friction
models. The simulation-to-reality gap is then differentiated with respect to
the unknown mass and friction parameters, and the analytically computed
gradient is used to optimize those parameters. Since it is difficult to
disentangle the mass from the friction coefficients in low-data and
quasi-static motion regimes, our approach retains a set of locally optimal
pairs of mass and friction models. A probability distribution on the models is
computed based on the relative accuracy of each pair of models. In the
exploitation phase, a probabilistic planner is used to select a goal
configuration and waypoints that are stable with a high confidence. The
proposed technique is evaluated on real objects and using a real manipulator.
The results show that this technique can not only identify accurately mass and
friction coefficients of non-uniform heterogeneous objects, but can also be
used to successfully slide an unknown object to the edge of a table and pick it
up from there, without any human assistance or feedback.
