End-to-End Imitation Learning for Optimal Asteroid Proximity Operations

2025/02/03 by George Nehma, Quinn, Patrick, Madhur Tiwari +2 · 1 citation
Engineering · Physics and Astronomy · #Astro and Planetary Science #FOS: Computer and information sciences #I.2.9 #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods #Robotics (cs.RO) #Space Satellite Systems and Control

paper · pdf · doi:10.48550/arxiv.2502.01034

openalex publication_date 2025/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Controlling spacecraft near asteroids in deep space comes with many challenges. The delays involved necessitate heavy usage of limited onboard computation resources while fuel efficiency remains a priority to support the long loiter times needed for gathering data. Additionally, the difficulty of state determination due to the lack of traditional reference systems requires a guidance, navigation, and control (GNC) pipeline that ideally is both computationally and fuel-efficient, and that incorporates a robust state determination system. In this paper, we propose an end-to-end algorithm utilizing neural networks to generate near-optimal control commands from raw sensor data, as well as a hybrid model predictive control (MPC) guided imitation learning controller delivering improvements in computational efficiency over a traditional MPC controller.

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