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

arXiv: https://arxiv.org/abs/2509.04628 · HTML: https://mlsanity.com/paper/2509.04628/

## 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.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges](https://mlsanity.com/paper/2309.02473/) (2023)
- [Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation](https://mlsanity.com/paper/2011.06719/) (2020)
- [All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL](https://mlsanity.com/paper/2202.11960/) (2022)
- [Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning](https://mlsanity.com/paper/2008.03215/) (2020)
- [Towards Resilient Tracking in Autonomous Vehicles: A Distributionally Robust Input and State Estimation Approach](https://mlsanity.com/paper/2504.09974/) (2025)
- [Imitation Learning for Satellite Attitude Control under Unknown Perturbations](https://mlsanity.com/paper/2507.01161/) (2025)
- [End-to-End Imitation Learning for Optimal Asteroid Proximity Operations](https://mlsanity.com/paper/2502.01034/) (2025)

## Built on by (most cited first, 2 of 2)

- [Tiny Recursive Control: Iterative Reasoning for Efficient Optimal Control](https://mlsanity.com/paper/2512.16824/) (2025)
- [Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration](https://mlsanity.com/paper/2601.12952/) (2026)

## 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.