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.
@article{arxiv.1806.06003,
title = {On Machine Learning and Structure for Mobile Robots},
author = {Markus Wulfmeier},
journal= {arXiv preprint arXiv:1806.06003},
year = {2018}
}