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Building intelligent autonomous systems at any scale is challenging. Thesensing and computation constraints of a microrobot platform make the problemsharder. We present improvements to learning-based methods for on-board learningof locomotion, classification, and navigation of microrobots. We show howsimulated locomotion can be achieved with model-based reinforcement learningvia on-board sensor data distilled into control. Next, we introduce a sparse,linear detector and a Dynamic Thresholding method to FAST Visual Odometry forimproved navigation in the noisy regime of mm scale imagery. We end with a newimage classifier capable of classification with fewer than one millionmultiply-and-accumulate (MAC) operations by combining fast downsampling,efficient layer structures and hard activation functions. These are promisingsteps toward using state-of-the-art algorithms in the power-limited world ofedge-intelligence and microrobots.

Learning for Microrobot Exploration: Model-based Locomotion, Sparse-robust Navigation, and Low-power Deep Classification