  The transfer of a robot skill between different geometric environments is
non-trivial since a wide variety of environments exists, sensor observations as
well as robot motions are high-dimensional, and the environment might only be
partially observed. We consider the problem of extracting a low-dimensional
description of the manipulated environment in form of a kinematic model. This
allows us to transfer a skill by defining a policy on a prototype model and
morphing the observed environment to this prototype. A deep neural network is
used to map depth image observations of the environment to morphing parameter,
which include transformation and configuration parameters of the prototype
model. Using the concatenation property of affine transformations and the
ability to convert point clouds to depth images allows to apply the network in
an iterative manner. The network is trained on data generated in a simulator
and on augmented data that is created by using network predictions. The
algorithm is evaluated on different tasks, where it is shown that iterative
predictions lead to a higher accuracy than one-step predictions.
