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Autonomous micromobility has been attracting the attention of researchers andpractitioners in recent years. A key component of many micro-transport vehiclesis the DC motor, a complex dynamical system that is continuous and non-linear.Learning to quickly control the DC motor in the presence of disturbances anduncertainties is desired for various applications that require robustness andstability. Techniques to accomplish this task usually rely on a mathematicalsystem model, which is often insufficient to anticipate the effects oftime-varying and interrelated sources of non-linearities. While some model-freeapproaches have been successful at the task, they rely on massive interactionswith the system and are trained in specialized hardware in order to fit ahighly parameterized controller. In this work, we learn to steer a DC motor viasample-efficient reinforcement learning. Using data collected from hardwareinteractions in the real world, we additionally build a simulator to experimentwith a wide range of parameters and learning strategies. With the bestparameters found, we learn an effective control policy in one minute and 53seconds on a simulation and in 10 minutes and 35 seconds on a physical system.

Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning