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.

    Graphical abstract Display Omitted

    Highlights Imitation learning can produce optimal feedback controllers for the pinpoint landing problem. Shifts in state distributions between training and rollout can cause poor performance. Gaussian Mixture Models can fit to state distributions. Approximations of the Kullback–Leibler divergence show higher shift for some landing formulations/policies than others.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Metric to evaluate distribution shift from behavioral cloning for fuel-optimal landing policies


    Contributors:

    Published in:

    Acta Astronautica ; 203 ; 421-428


    Publication date :

    2022-12-09


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English







    Deep L 1 Stochastic Optimal Control Policies for Planetary Soft Landing

    Pereira, Marcus Aloysius / Duarte, Camilo A. / Theodorou, Evangelos A. et al. | AIAA | 2024


    Method and system for behavioral cloning of autonomous driving policies for safe autonomous agents

    BUEHLER ANDREAS / GAIDON ADRIEN DAVID / AMBRUS RARES A et al. | European Patent Office | 2023

    Free access