Imitation learning-based spacecraft rendezvous and docking method with Expert Demonstration

2026-01-19

AI Summary

This work proposes a model-free, six-degree-of-freedom control framework for spacecraft rendezvous and docking based on imitation learning (IL-SRD), addressing the limited robustness of conventional approaches that rely heavily on precise dynamical models in uncertain orbital environments. By directly learning docking policies from expert demonstrations, the method reduces dependence on high-fidelity modeling. It introduces an anchored decoding target mechanism to constrain control generation and integrates a temporal aggregation scheme to mitigate error accumulation inherent in Transformer-based sequence prediction. Simulation results demonstrate that the proposed approach achieves high-precision, energy-efficient control while exhibiting superior robustness under unknown disturbances.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Existing spacecraft rendezvous and docking control methods largely rely on predefined dynamic models and often exhibit limited robustness in realistic on-orbit environments. To address this issue, this paper proposes an Imitation Learning-based spacecraft rendezvous and docking control framework (IL-SRD) that directly learns control policies from expert demonstrations, thereby reducing dependence on accurate modeling. We propose an anchored decoder target mechanism, which conditions the decoder queries on state-related anchors to explicitly constrain the control generation process. This mechanism enforces physically consistent control evolution and effectively suppresses implausible action deviations in sequential prediction, enabling reliable six-degree-of-freedom (6-DOF) rendezvous and docking control. To further enhance stability, a temporal aggregation mechanism is incorporated to mitigate error accumulation caused by the sequential prediction nature of Transformer-based models, where small inaccuracies at each time step can propagate and amplify over long horizons. Extensive simulation results demonstrate that the proposed IL-SRD framework achieves accurate and energy-efficient model-free rendezvous and docking control. Robustness evaluations further confirm its capability to maintain competitive performance under significant unknown disturbances. The source code is available at https://github.com/Dongzhou-1996/IL-SRD.

Problem

• spacecraft rendezvous and docking
• robustness
• model dependence
• on-orbit environment
• control policy

Innovation

• Imitation Learning
• Spacecraft Rendezvous and Docking
• Anchored Decoder Target
• Temporal Aggregation
• Model-Free Control