  We propose a new technique for pushing an unknown object from an initial
configuration to a goal configuration with stability constraints. The proposed
method leverages recent progress in differentiable physics models to learn
unknown mechanical properties of pushed objects, such as their distributions of
mass and coefficients of friction. The proposed learning technique computes the
gradient of the distance between predicted 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. The proposed approach is
also utilized to optimize a policy to efficiently push an object toward the
desired goal configuration. Experiments with real objects using a real robot to
gather data show that the proposed approach can identify the mechanical
properties of heterogeneous objects from a small number of pushing actions.
