A significant amount of grad student time is sunk into being human PID loops and sensor fusion models for their experiments. Repetition makes humans dumber and/

A significant amount of grad student time is sunk into being human PID loops and sensor fusion models for their experiments. Repetition makes humans dumber and/or depressed. The search space is limited to human scales and predictive models with may rely on hidden variables. These hidden variables may also necessitate more sensitive electronics to sample and introduce timing constraints on the experiment design. It is ripe territory to create automated experimenters that generate PID loop algorithms and gradient fields without accounting for hidden or difficult-to-measure variables of the environment. These algorithms are not too complicated and we can already see it in fuzzing / concolic execution platforms for discovering security vulnerabilities. This concept can be applied across disciplines. In this paper’s case, we this concept is applied to a robot (an STM) to control for hidden forces impacting the atoms and biasing its own output. These forces change over time. Source code, highly legible, good references for a beginner, even included the controller code… this is research!