Models of aeolian processes rely on accurate measurements of the rates of sediment transport by wind, and careful evaluation of the environmental controls of these processes. Existing field approaches typically require intensive, event-based experiments involving dense arrays of instruments. These devices are often cumbersome and logistically difficult to set up and maintain, especially near steep or vegetated dune surfaces. Significant advances in instrumentation are needed to provide the datasets that are required to validate and improve mechanistic models of aeolian sediment transport. Recent advances in robotics show great promise for assisting and amplifying scientists’ efforts to increase the spatial and temporal resolution of many environmental measurements governing sediment transport. The emergence of cheap, agile, human-scale robotic platforms endowed with increasingly sophisticated sensor and motor suites opens up the prospect of deploying programmable, reactive sensor payloads across complex terrain in the service of aeolian science. This paper surveys the need and assesses the opportunities and challenges for amassing novel, highly resolved spatiotemporal datasets for aeolian research using partially-automated ground mobility. We review the limitations of existing measurement approaches for aeolian processes, and discuss how they may be transformed by ground-based robotic platforms, using examples from our initial field experiments. We then review how the need to traverse challenging aeolian terrains and simultaneously make high-resolution measurements of critical variables requires enhanced robotic capability. Finally, we conclude with a look to the future, in which robotic platforms may operate with increasing autonomy in harsh conditions. Besides expanding the completeness of terrestrial datasets, bringing ground-based robots to the aeolian research community may lead to unexpected discoveries that generate new hypotheses to expand the science itself. For more information: Kod*lab (http://kodlab.seas.upenn.edu/)