mlsanityData to 14 Oct 2026

Action Chunking with Transformers for Image-Based Spacecraft Guidance and Control

arXiv 2509.04628 ↗First posted 4 Sep 2025cs.RO cs.AIcited by 2

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

also in 1 more
Abstract (arXiv)

We present an imitation learning approach for spacecraft guidance, navigation, and control(GNC) that achieves high performance from limited data. Using only 100 expert demonstrations, equivalent to 6,300 environment interactions, our method, which implements Action Chunking with Transformers (ACT), learns a control policy that maps visual and state observations to thrust and torque commands. ACT generates smoother, more consistent trajectories than a meta-reinforcement learning (meta-RL) baseline trained with 40 million interactions. We evaluate ACT on a rendezvous task: in-orbit docking with the International Space Station (ISS). We show that our approach achieves greater accuracy, smoother control, and greater sample efficiency.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 19 references that are in the map. First posted on arXiv 4 Sep 2025.

Builds on

9 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Imitation Learning for Satellite Attitude Control under Unknown Perturbations1 Jul
  2. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges5 Sep

Other references · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,568
  3. 2020Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulationcited by 38
  4. 2022All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLcited by 9
  5. 2020Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learningcited by 4
  6. 2025Towards Resilient Tracking in Autonomous Vehicles: A Distributionally Robust Input and State Estimation Approachcited by 2
  7. 2025End-to-End Imitation Learning for Optimal Asteroid Proximity Operationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

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

2 papers.

  1. 2025Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Controlcited by 4
  2. 2026Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstrationcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025RL-AVIST: Reinforcement Learning for Autonomous Visual Inspection of Space Targets
  2. 2025Comparing Behavioural Cloning and Reinforcement Learning for Spacecraft Guidance and Control Networks
  3. 2025Multi-Phase Spacecraft Trajectory Optimization via Transformer-Based Reinforcement Learning
  4. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrations
  5. 2026Task-Space Imitation Guidance for Efficient Reinforcement Learning
  6. 2026TRACT: Temporally Routed Action Chunks with Chronological Phase Authority for Contact-Rich Manipulation
  7. 2021Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning
  8. 2026Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks