Learning for Control of Rolling ubots

Micron-scale robots (ubots) have recently shown great promise for emerging medical applications, and accurate control of ubots is a critical next step to deploying them in real systems. In this work, we develop the idea of a nonlinear mismatch controller to compensate for the mismatch between the disturbed unicycle model of a rolling ubot and trajectory data collected during an experiment. We exploit the differential flatness property of the rolling ubot model to generate a mapping from the desired state trajectory to nominal control actions. Due to model mismatch and parameter estimation error, the nominal control actions will not exactly reproduce the desired state trajectory. We employ a Gaussian Process (GP) to learn the model mismatch as a function of the desired control actions, and correct the nominal control actions using a least-squares optimization. We demonstrate the performance of our online learning algorithm in simulation, where we show that the model mismatch makes some desired states unreachable. Finally, we validate our approach in an experiment and show that the error metrics are reduced by up to 40%.