  Ground robots which are able to navigate a variety of terrains are needed in
many domains. One of the key aspects is the capability to adapt to the ground
structure, which can be realized through movable body parts coming along with
additional degrees of freedom (DoF). However, planning respective locomotion is
challenging since suitable representations result in large state spaces.
Employing an additional abstract representation---which is coarser,
lower-dimensional, and semantically enriched---can support the planning.
  While a desired robot representation and action set of such an abstract
representation can be easily defined, the cost function requires large tuning
efforts. We propose a method to represent the cost function as a CNN. Training
of the network is done on generated artificial data, while it generalizes well
to the abstraction of real world scenes. We further apply our method to the
problem of search-based planning of hybrid driving-stepping locomotion. The
abstract representation is used as a powerful informed heuristic which
accelerates planning by multiple orders of magnitude.
