  We consider the problem of a robot learning the mechanical properties of
objects through physical interaction with the object, and introduce a
practical, data-efficient approach for identifying the motion models of these
objects. The proposed method utilizes a physics engine, where the robot seeks
to identify the inertial and friction parameters of the object by simulating
its motion under different values of the parameters and identifying those that
result in a simulation which matches the observed real motions. The problem is
solved in a Bayesian optimization framework. The same framework is used for
both identifying the model of an object online and searching for a policy that
would minimize a given cost function according to the identified model.
Experimental results both in simulation and using a real robot indicate that
the proposed method outperforms state-of-the-art model-free reinforcement
learning approaches.
