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

arXiv: https://arxiv.org/abs/2502.01034 · HTML: https://mlsanity.com/paper/2502.01034/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [Learning by Cheating](https://mlsanity.com/paper/1912.12294/) (2019)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [Query-Efficient Imitation Learning for End-to-End Autonomous Driving](https://mlsanity.com/paper/1605.06450/) (2016)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systems](https://mlsanity.com/paper/2110.03506/) (2021)
- [Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models](https://mlsanity.com/paper/2409.16663/) (2024)
- [Global overview of Imitation Learning](https://mlsanity.com/paper/1801.06503/) (2018)
- [Closing the Loop on Runtime Monitors with Fallback-Safe MPC](https://mlsanity.com/paper/2309.08603/) (2023)
- [Safe end-to-end imitation learning for model predictive control](https://mlsanity.com/paper/1803.10231/) (2018)
- [Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racing](https://mlsanity.com/paper/2303.02267/) (2023)
- [Learning Agile, Vision-based Drone Flight: from Simulation to Reality](https://mlsanity.com/paper/2304.04128/) (2023)

## Built on by (most cited first, 1 of 1)

- [Action Chunking with Transformers for Image-Based Spacecraft Guidance and Control](https://mlsanity.com/paper/2509.04628/) (2025)

## Abstract (arXiv)

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.