SpaceMind让太空机器人自主感知、推理并自我进化，无需重写代码即可在真实和模拟环境中稳定执行任务。
SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
- 将技能、工具与推理分离为可独立扩展的模块，支持动态路由与配置化调用。
- 在真实与仿真环境中实现90%~100%导航成功率，极端条件下仍能完成搜索与逼近任务。
- 通过经验自演化机制，仅凭一次失败就能恢复，适合航天器自主维护场景。
自主在轨服务需要具备视觉感知、三维空间推理与长时程多阶段任务执行能力的具身智能体。我们提出SpaceMind，一种模块化且自进化的视觉语言模型（VLM）智能体框架，将知识、工具与推理分解为三个可独立扩展的维度：具有动态路由的技能模块、具有可配置配置文件的模型上下文协议（MCP）工具，以及可注入的推理模式技能。一个MCP-Redis接口层使得同一代码库可在仿真与物理硬件间无修改运行，而技能自演化机制则在不微调模型的前提下，将操作经验提炼为持久的技能文件。我们在五颗卫星、三种任务类型、两个环境（UE5仿真与物理实验室）中进行了192次闭环测试，刻意引入退化条件以验证鲁棒性。在正常条件下，所有模式均实现90%–100%导航成功率；在退化条件下，前瞻性模式在其他模式失败的任务中仍成功完成搜索与逼近。自演化实验表明，六组中有四组仅通过一次失败即实现从完全失败到100%成功的恢复，巡检评分从12提升至59（满分100）。真实世界验证确认了零代码修改迁移至物理机器人，并实现100%交会成功。代码：https://github.com/wuaodi/SpaceMind
原文摘要 · Abstract (English)
Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase tasks over extended horizons. We present SpaceMind, a modular and self-evolving vision-language model (VLM) agent framework that decomposes knowledge, tools, and reasoning into three independently extensible dimensions: skill modules with dynamic routing, Model Context Protocol (MCP) tools with configurable profiles, and injectable reasoning-mode skills. An MCP-Redis interface layer enables the same codebase to operate across simulation and physical hardware without modification, and a Skill Self-Evolution mechanism distills operational experience into persistent skill files without model fine-tuning. We validate SpaceMind through 192 closed-loop runs across five satellites, three task types, and two environments, a UE5 simulation and a physical laboratory, deliberately including degraded conditions to stress-test robustness. Under nominal conditions all modes achieve 90--100% navigation success; under degradation, the Prospective mode uniquely succeeds in search-and-approach tasks where other modes fail. A self-evolution study shows that the agent recovers from failure in four of six groups from a single failed episode, including complete failure to 100% success and inspection scores improving from 12 to 59 out of 100. Real-world validation confirms zero-code-modification transfer to a physical robot with 100% rendezvous success. Code: https://github.com/wuaodi/SpaceMind
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