面向在轨服务场景，围绕空间感知中复杂指令解析与轻量化部署两类关键挑战，系统综述了复杂指令驱动下基础视觉模型的协同解析模式及其面向星载受限平台的轻量化部署进展。研究对比了各类协同解析模式在航天约束下的适用场景与算力开销，针对星载芯片的“内存墙”壁垒，系统阐述了软硬协同轻量化架构，并结合太空高风险特性，探讨了基于不确定性感知的分级回退机制与现有评测基准的局限。综合分析表明，合理调配协同解析模式，并将软硬协同轻量化与不确定性感知回退机制结合，是应对星载资源受限与环境不确定性的关键路径；同时，具身感知、多模态协同与高保真合成数据将成为未来重要研究方向。本文紧扣航天物理约束，系统梳理了从复杂指令解析、星载轻量化部署到可靠性保障闭环的关键技术链路，形成了面向在轨感知系统的综合评估视角，为下一代自主化在轨感知系统研究提供了思路与参考。

Targeting on-orbit servicing scenarios and focusing on two key challenges of complex instruction parsing and lightweight deployment in space perception, this paper provides a systematic review of the collaborative parsing paradigms of vision foundation models driven by complex instructions, along with progress in their lightweight deployment on resource-constrained spaceborne platforms. The study compares the applicable scenarios and computational overhead of various collaborative segmentation paradigms under aerospace constraints. To address the \"memory wall\" bottleneck of spaceborne chips, it systematically elaborates on software-hardware co-designed lightweight architectures. Additionally, considering the high-risk characteristics of the space environment, it explores uncertainty-aware hierarchical fallback mechanisms and identifies the limitations of current evaluation benchmarks. Comprehensive analysis demonstrates that appropriately coordinating collaborative parsing paradigms and integrating software-hardware lightweight architectures with uncertainty-aware fallback mechanisms constitute a crucial pathway to addressing spaceborne resource constraints and environmental unpredictability. Meanwhile, embodied perception, multimodal collaboration, and high-fidelity synthetic data will emerge as pivotal directions for future research. This study closely adheres to aerospace physical constraints and systematically maps the critical technical links－from complex instruction parsing and spaceborne lightweight deployment to a closed-loop reliability assurance. It establishes a comprehensive evaluation perspective for on-orbit perception systems, providing insights and references for research on next-generation autonomous on-orbit perception systems.