  Due to recent advances - compute, data, models - the role of learning in
autonomous systems has expanded significantly, rendering new applications
possible for the first time. While some of the most significant benefits are
obtained in the perception modules of the software stack, other aspects
continue to rely on known manual procedures based on prior knowledge on
geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in
these modules when data collection and curation become easier than manual rule
design. Building on this coarse and broad survey of current research, the final
sections aim to provide insights into future potentials and challenges as well
as the necessity of structure in current practical applications.
