mlsanityData to 7 Oct 2026

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

arXiv 2502.01034 ↗First posted 3 Feb 2025cs.RO cs.LGcited by 1

Not on the map. Why?

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.

Key facts. Cited by 1 paper 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

12 of 20 references.

References · 12

  1. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  2. 2019Learning by Cheatingcited by 361
  3. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 145
  4. 2016Query-Efficient Imitation Learning for End-to-End Autonomous Drivingcited by 83
  5. 2020Deep Drone Acrobaticscited by 66
  6. 2021Run Time Assurance for Safety-Critical Systems: An Introduction to Safety Filtering Approaches for Complex Control Systemscited by 25
  7. 2024Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Modelscited by 22
  8. 2018Global overview of Imitation Learningcited by 16
  9. 2023Closing the Loop on Runtime Monitors with Fallback-Safe MPCcited by 14
  10. 2018Safe end-to-end imitation learning for model predictive controlcited by 12
  11. 2023Towards Safety Assured End-to-End Vision-Based Control for Autonomous Racingcited by 4
  12. 2023Learning Agile, Vision-based Drone Flight: from Simulation to Realitycited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Action Chunking with Transformers for Image-Based Spacecraft Guidance and Controlcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2026Safety-Guaranteed Imitation Learning from Nonlinear Model Predictive Control for Spacecraft Close Proximity Operations
  2. 2026Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration
  3. 2019Learning the optimal state-feedback via supervised imitation learning
  4. 2026GPU-Accelerated Astrodynamics World Models for Spacecraft Rendezvous and Proximity Operations
  5. 2021Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
  6. 2025Imitation Learning for Satellite Attitude Control under Unknown Perturbations
  7. 2023Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation
  8. 2022Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation