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Data driven robotics relies upon accurate real-world representations to learnuseful policies. Despite our best-efforts, zero-shot sim-to-real transfer isstill an unsolved problem, and we often need to allow our agents to exploreonline to learn useful policies for a given task. For many applications offield robotics online exploration is prohibitively expensive and dangerous,this is especially true in fixed-wing aerial robotics. To address thesechallenges we offer an intermediary solution for learning in field robotics. Weinvestigate the use of dissimilar platform vehicle for learning and offer aprocedure to mimic the behavior of one vehicle with another. We specificallyconsider the problem of training fixed-wing aircraft, an expensive anddangerous vehicle type, using a multi-rotor host platform. Using a ModelPredictive Control approach, we design a controller capable of mimickinganother vehicles behavior in both simulation and the real-world.

Quad2Plane: An Intermediate Training Procedure for Online Exploration in Aerial Robotics via Receding Horizon Control