Abstract
The development of robust fuel-optimal feedback controllers for the pinpoint landing problem has application to a variety of aerospace vehicles including lunar and planetary landers. Imitation learning has been used for a variety of low-fidelity lander formulations to train a policy based on numerically generated open-loop optimal trajectories. The simplest technique, Behavioral Cloning, can suffer from distribution shift, where the distribution of training data is different than the distribution of states seen in rollout. This paper presents a new metric to measure this shift, and a comprehensive study is performed on a variety of lander formulations and policy classes. It is found that shift increases with dimensionality of and the level of coupling in equations of motion, and that the non-parametric policy yields more shift than the parametric one.
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