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Due to recent advances - compute, data, models - the role of learning inautonomous systems has expanded significantly, rendering new applicationspossible for the first time. While some of the most significant benefits areobtained in the perception modules of the software stack, other aspectscontinue to rely on known manual procedures based on prior knowledge ongeometry, dynamics, kinematics etc. Nonetheless, learning gains relevance inthese modules when data collection and curation become easier than manual ruledesign. Building on this coarse and broad survey of current research, the finalsections aim to provide insights into future potentials and challenges as wellas the necessity of structure in current practical applications.

On Machine Learning and Structure for Mobile Robots