On Machine Learning and Structure for Mobile Robots
This is an incremental survey paper that provides insights into the role of learning and structure in mobile robotics for researchers and practitioners.
This paper surveys how machine learning is expanding in mobile robot systems, particularly in perception modules where it enables new applications, while noting that other aspects still rely on manual procedures based on prior knowledge.
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.