Exploration of small body systems poses the problem of designing path planning strategies for possibly uncharted environments. Traditional methods aimed at developing rigorous trajectory baselines may suffer inefficiencies, or turn infeasible when confronted with unknown dynamics. In strongly non-linear dynamics, mapping point design solutions from one dynamical regime to another may be hindered by underlying chaotic behavior. Rather than relying on baseline driven approaches, more generalized strategies may be found by observing human pilots controlling spacecraft motion within varying dynamical environments; the resultant data can then be utilized to initialize machine learning agents to provide more autonomous solutions. A previous numerical experiment resulted in a technical dataset comprising of human-based path planning strategies across a range of binary asteroid systems. This dataset is now used to train various imitation learning agents, and initiate the creation of a framework that integrates human–machine cooperation into the early training phases of artificial intelligent agents; the current application is for spacecraft guidance in binary asteroid systems, as a prototype of complex, potentially unknown, orbit dynamics. An interactive training architecture, based on the DAgger algorithm, is designed and employed to train both original and interactively coached agents, the latter stemming from both corrective and evaluative feedback by a real time human interactor. All agents were interactively trained for a predefined time period. The results from this investigation may provide the first, empirical observations of behavioral cloning within multi-body dynamics with largely randomized parameters, with some notable contributions including early characterization of training time, initial evidence of an autonomous agent learning meaningful policy features via imitation, and early identification of challenges in training fully autonomous agents for a multi-body dynamics path planning problem of this complexity and high dimensional state space.

对小身体系统的探索提出了为可能未知的环境设计路径规划策略的问题。旨在开发严格轨迹基线的传统方法可能效率低下，或者在遇到未知动态时变得不可行。在强非线性动力学中，将点设计解决方案从一种动力学状态映射​​到另一种可能会受到潜在混沌行为的阻碍。通过观察人类飞行员在不同动态环境中控制航天器运动，可以找到更通用的策略，而不是依赖基线驱动的方法；然后可以利用生成的数据来初始化机器学习代理，以提供更自主的解决方案。先前的数值实验产生了一个技术数据集，其中包括跨一系列双小行星系统的基于人类的路径规划策略。该数据集现在用于训练各种模仿学习代理，并开始创建一个框架，将人机合作整合到人工智能代理的早期训练阶段；目前的应用是用于双小行星系统中的航天器制导，作为复杂的、可能未知的轨道动力学原型。基于 DAgger 算法的交互式训练架构被设计和用于训练原始和交互式指导的代理，后者源自实时人类交互者的纠正和评估反馈。在预定义的时间段内对所有代理进行交互式培训。