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

Alejandro Posadas-Nava, Andrea Scorsoglio, Luca Ghilardi, Roberto Furfaro, Richard Linares
9/4/2025
cs.ROcs.AI

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Abstract

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.

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Cite this paper

@article{posadasnava2025action,
  title  = {Action Chunking with Transformers for Image-Based Spacecraft Guidance and Control},
  author = {Alejandro Posadas-Nava and Andrea Scorsoglio and Luca Ghilardi and Roberto Furfaro and Richard Linares},
  year   = {2025},
  eprint = {2509.04628},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.04628v1}
}

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