mlsanityData to 7 Oct 2026

Imitation Learning for Satellite Attitude Control under Unknown Perturbations

arXiv 2507.01161 ↗First posted 1 Jul 2025eess.SY cs.RO cs.SYcited by 2

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

This paper presents a novel satellite attitude control framework that integrates Soft Actor-Critic (SAC) reinforcement learning with Generative Adversarial Imitation Learning (GAIL) to achieve robust performance under various unknown perturbations. Traditional control techniques often rely on precise system models and are sensitive to parameter uncertainties and external perturbations. To overcome these limitations, we first develop a SAC-based expert controller that demonstrates improved resilience against actuator failures, sensor noise, and attitude misalignments, outperforming our previous results in several challenging scenarios. We then use GAIL to train a learner policy that imitates the expert's trajectories, thereby reducing training costs and improving generalization through expert demonstrations. Preliminary experiments under single and combined perturbations show that the SAC expert can rotate the antenna to a specified direction and keep the antenna orientation reliably stable in most of the listed perturbations. Additionally, the GAIL learner can imitate most of the features from the trajectories generated by the SAC expert. Comparative evaluations and ablation studies confirm the effectiveness of the SAC algorithm and reward shaping. The integration of GAIL further reduces sample complexity and demonstrates promising imitation capabilities, paving the way for more intelligent and autonomous spacecraft control systems.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 18 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

6 of 18 references.

References · 6

  1. 2016Generative Adversarial Imitation Learningcited by 1,576
  2. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  3. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  4. 2024Reinforcement learningcited by 236
  5. 2017Coordinated Multi-Agent Imitation Learningcited by 70
  6. 2023Adaptive Compatible Performance Control for Spacecraft Attitude Control under Motion Constraints with Guaranteed Accuracycited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Action Chunking with Transformers for Image-Based Spacecraft Guidance and Controlcited by 2
  2. 2026Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Controlcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2019Learning the optimal state-feedback via supervised imitation learning
  2. 2026Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration
  3. 2020When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence
  4. 2021GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback
  5. 2025Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks
  6. 2020On Computation and Generalization of Generative Adversarial Imitation Learning
  7. 2024C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
  8. 2020f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning