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

Agentic AI

Published: arXiv: 2509.04628v1

Authors

Alejandro Posadas-Nava Andrea Scorsoglio Luca Ghilardi Roberto Furfaro Richard Linares

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.

Paper Summary

Problem

The development of autonomous spacecraft guidance and control (GNC) systems is a significant challenge in modern space exploration. Spacecraft must operate independently due to limitations in communication and unpredictable environments, making traditional ground-controlled operations unsuitable.

Key Innovation

This paper presents a hybrid learning pipeline that uses meta-reinforcement learning (meta-RL) to generate expert trajectories, which are then distilled into deployable control policies using Action Chunking Transformers (ACT). This approach enables the training of precise and smooth control policies with limited data and improved sample efficiency.

Practical Impact

The proposed method has the potential to improve the autonomy and precision of spacecraft guidance and control systems. By reducing the amount of expert demonstrations required for training, this approach can be applied to various space exploration missions, such as in-orbit docking and proximity operations.

Analogy / Intuitive Explanation

Imagine trying to learn a new dance move by watching a professional dancer perform it several times. You wouldn't need to practice the entire routine yourself; instead, you could focus on breaking down the move into smaller chunks and practicing those individual steps. This is similar to how ACT works: it takes expert demonstrations (the professional dancer) and distills them into deployable control policies (your own dance moves).

Paper Information

Categories:

cs.RO cs.AI

Published Date:

arXiv ID:

2509.04628v1

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