# Autonomous Six-Degree-of-Freedom Spacecraft Docking Maneuvers via Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.03215 · HTML: https://mlsanity.com/paper/2008.03215/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment](https://mlsanity.com/paper/2407.03224/) (2024)
- [Action Chunking with Transformers for Image-Based Spacecraft Guidance and Control](https://mlsanity.com/paper/2509.04628/) (2025)
- [Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control](https://mlsanity.com/paper/2311.08943/) (2023)
- [On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making](https://mlsanity.com/paper/2409.17125/) (2024)

## Abstract (arXiv)

A policy for six-degree-of-freedom docking maneuvers is developed through reinforcement learning and implemented as a feedback control law. Reinforcement learning provides a potential framework for robust, autonomous maneuvers in uncertain environments with low on-board computational cost. Specifically, proximal policy optimization is used to produce a docking policy that is valid over a portion of the six-degree-of-freedom state-space while striving to minimize performance and control costs. Experiments using the simulated Apollo transposition and docking maneuver exhibit the policy's capabilities and provide a comparison with standard optimal control techniques. Furthermore, specific challenges and work-arounds, as well as a discussion on the benefits and disadvantages of reinforcement learning for docking policies, are discussed to facilitate future research. As such, this work will serve as a foundation for further investigation of learning-based control laws for spacecraft proximity operations in uncertain environments.