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A majority of microrobots are constructed using compliant materials that aredifficult to model analytically, limiting the utility of traditionalmodel-based controllers. Challenges in data collection on microrobots and largeerrors between simulated models and real robots make current model-basedlearning and sim-to-real transfer methods difficult to apply. We propose anovel framework residual model learning (RML) that leverages approximate modelsto substantially reduce the sample complexity associated with learning anaccurate robot model. We show that using RML, we can learn a model of theHarvard Ambulatory MicroRobot (HAMR) using just 12 seconds of passivelycollected interaction data. The learned model is accurate enough to beleveraged as "proxy-simulator" for learning walking and turning behaviors usingmodel-free reinforcement learning algorithms. RML provides a general frameworkfor learning from extremely small amounts of interaction data, and ourexperiments with HAMR clearly demonstrate that RML substantially outperformsexisting techniques.

Residual Model Learning for Microrobot Control