身体化知能のためのメモリネイティブ非地上ネットワーク
Memory-Native Non-Terrestrial Networks for Embodied Intelligence
非地上ネットワーク(NTN)に長期記憶を組み込んだメモリネイティブなパラダイムを提案し、ロボットの遠隔協調やクラウド利用を効率化する。物理記憶とデジタル記憶の二重アーキテクチャにより、動的環境下での意思決定を改善する。
著者: Chengyang Li, Yikun Wang, Jiahui He, Yujie Wan, Shuai Wang, Yuan Wu, Yik-Chung Wu, Chengzhong Xu, Huseyin Arslan
分類: cs.RO, cs.AI, cs.MA, cs.NI
原文アブストラクト
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.