  Building intelligent autonomous systems at any scale is challenging. The
sensing and computation constraints of a microrobot platform make the problems
harder. We present improvements to learning-based methods for on-board learning
of locomotion, classification, and navigation of microrobots. We show how
simulated locomotion can be achieved with model-based reinforcement learning
via on-board sensor data distilled into control. Next, we introduce a sparse,
linear detector and a Dynamic Thresholding method to FAST Visual Odometry for
improved navigation in the noisy regime of mm scale imagery. We end with a new
image classifier capable of classification with fewer than one million
multiply-and-accumulate (MAC) operations by combining fast downsampling,
efficient layer structures and hard activation functions. These are promising
steps toward using state-of-the-art algorithms in the power-limited world of
edge-intelligence and microrobots.
