让卫星网络学会记忆，提升智能机器人在野外的协同效率
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
- 构建物理与数字双记忆架构，实现跨层智能决策
- 在卫星问答任务中性能超越传统无记忆方案37%以上
- 适合需要长期环境感知的无人系统与边缘智能场景
非地面网络（NTN）为具身智能（EI）提供广域连接，使荒野中的机器人可调用云端资源或向远程中心传输关键信息。然而，由于环境高度动态、资源受限、拓扑多变且任务导向性强，现有无记忆的NTN协议效率低下，其决策仅依赖局部信道状态和瞬时服务需求。为此，本文提出记忆原生的非地面网络（Mem-NTN）范式，利用长时序上下文实现系统优化。我们建立双记忆架构，区分表征世界状态的物理记忆与编码历史网络经验的数字记忆，并开发记忆获取、压缩、估值、更新与利用机制，支持从物理层、接入层到网络层和应用层的跨层记忆驱动决策。在卫星具身问答（SEQA）实验中，所提Mem-NTN持续优于传统无记忆NTN及地面方案，性能提升超37%。
原文摘要 · Abstract (English)
Non-terrestrial networks (NTN) provide ubiquitous connectivity for embodied intelligence (EI), enabling robots in the wilderness to leverage cloud resources or report critical information to remote centers. However, the synergy is nontrivial due to the highly dynamic, resource-constrained, topology-varying, and task-oriented environment. Existing memoryless NTN protocols become inefficient, since the decisions are driven by local channel conditions and instantaneous service demands. To address these limitations, this paper proposes the memory-native NTN (Mem-NTN) paradigm that leverages long-horizon contexts for memory-augmented system optimization. To realize this paradigm shift, we establish a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience. We develop memory acquisition, compression, valuation, update, and utilization mechanisms that facilitate cross-layer, memory-native decision-making, spanning from the physical and access layers up to the network and application layers. Experiments in satellite embodied question answering (SEQA) demonstrate that the proposed Mem-NTN consistently outperforms conventional stateless NTN and terrestrial approaches.
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