  This paper proposes a new method for manipulating unknown objects through a
sequence of non-prehensile actions that displace an object from its initial
configuration to a given goal configuration on a flat surface. The proposed
method leverages recent progress in differentiable physics models to identify
unknown mechanical properties of manipulated objects, such as inertia matrix,
friction coefficients and external forces acting on the object. To this end, a
recently proposed differentiable physics engine for two-dimensional objects is
adopted in this work and extended to deal forces in the three-dimensional
space. The proposed model identification technique analytically computes the
gradient of the distance between forecasted poses of objects and their actual
observed poses and utilizes that gradient to search for values of the
mechanical properties that reduce the reality gap. Experiments with real
objects using a real robot to gather data show that the proposed approach can
identify the mechanical properties of heterogeneous objects on the fly.
