  A majority of microrobots are constructed using compliant materials that are
difficult to model analytically, limiting the utility of traditional
model-based controllers. Challenges in data collection on microrobots and large
errors between simulated models and real robots make current model-based
learning and sim-to-real transfer methods difficult to apply. We propose a
novel framework residual model learning (RML) that leverages approximate models
to substantially reduce the sample complexity associated with learning an
accurate robot model. We show that using RML, we can learn a model of the
Harvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passively
collected interaction data. The learned model is accurate enough to be
leveraged as "proxy-simulator" for learning walking and turning behaviors using
model-free reinforcement learning algorithms. RML provides a general framework
for learning from extremely small amounts of interaction data, and our
experiments with HAMR clearly demonstrate that RML substantially outperforms
existing techniques.
