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.

翻译：现有的航天器交会对接控制方法主要依赖于预定义动力学模型，在实际在轨环境中往往表现出有限的鲁棒性。为解决这一问题，本文提出一种基于模仿学习的航天器交会对接控制框架（IL-SRD），该框架直接从专家演示中学习控制策略，从而降低对精确建模的依赖。我们提出了一种锚定解码器目标机制，该机制将解码器查询条件建立在状态相关的锚点上，以显式约束控制生成过程。该机制强制实现物理一致的控制演化，并有效抑制序列预测中不合理的动作偏差，从而实现可靠的六自由度（6-DOF）交会对接控制。为进一步增强稳定性，本框架引入了时序聚合机制，以缓解基于Transformer的模型因序列预测特性导致的误差累积问题——即每个时间步的微小不准确性可能在长时域中传播并放大。大量仿真结果表明，所提出的IL-SRD框架能够实现精确且节能的无模型交会对接控制。鲁棒性评估进一步证实了其在显著未知扰动下保持竞争优势的能力。源代码发布于 https://github.com/Dongzhou-1996/IL-SRD。