Problem
Research questions and friction points this paper is trying to address.
Space Robots
Autonomy
Adaptability
AI-Enabled Operations
Mission Constraints
Artificial Intelligence-Enabled Space Robot Operations: Technologies, Challenges and Prospects
🤖 AI Summary
本文探讨了利用人工智能技术提高太空机器人自主性和适应性的问题，通过建立三层技术框架来应对太空特定约束和挑战。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-Enabled Space Robot Operations
Three-Layer Technical Framework
Continual Learning
Physically Constrained Policy Learning
Space Computing Infrastructures
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