人工智能赋能的太空机器人操作：技术、挑战与展望

中文摘要 AI 辅助

太空机器人日益被期望在有限的人类干预下执行长时间、高接触和多阶段的操作任务。人工智能（AI）、机器人学习以及具身基础模型的最新进展为提升此类系统的自主性和适应性提供了新的机遇，但其向太空的迁移受到稀缺的任务数据、太空特有的动力学与感知条件、有限星载资源以及严格安全性要求的制约。本文从能力构建的视角综述了人工智能赋能的太空机器人操作（AI-SRO）。我们首先总结了代表性操作场景、自主性趋势及太空特有约束。随后，我们建立了一个包含能力基础、能力形成和能力部署/演化的三层技术框架。在该框架内，我们综述了仿真环境、数据集和基准；任务与环境理解、状态感知、决策与规划以及动作执行；以及星载部署、天地适应、持续学习和能力迁移。最后，我们提出了面向可信仿真与数据、开放世界多模态认知、长时程安全决策、物理约束策略学习以及太空计算基础设施的关键研究方向。

英文摘要

Space robots are increasingly expected to perform long-duration, contact-rich, and multi-stage operations with limited human intervention. Recent advances in artificial intelligence (AI), robot learning, and embodied foundation models provide new opportunities to improve the autonomy and adaptability of such systems, but their transfer to space is constrained by scarce mission data, space-specific dynamics and sensing conditions, limited onboard resources, and stringent safety requirements. This article reviews artificial intelligence-enabled space robot operations (AI-SRO) from a capability-building perspective. We first summarize representative operational scenarios, autonomy trends, and space-specific constraints. We then establish a three-layer technical framework comprising capability foundations, capability formation, and capability deployment/evolution. Within this framework, we review simulation environments, datasets and benchmarks; task and environment understanding, state perception, decision-making and planning, and action execution; and onboard deployment, ground-to-space adaptation, continual learning, and capability transfer. Finally, we propose key research directions toward trustworthy simulation and data, open-world multimodal cognition, long-horizon safe decision-making, physically constrained policy learning, and space computing infrastructures.

发表机构

• Beijing University of Posts and Telecommunications（北京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。