Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels. In realistic collaborative sensing scenarios, spectral resources are quantized into finite physical resource blocks or orthogonal subcarriers, rendering simultaneous transmission by all agents infeasible. To address this, we propose Multi-Agent Semantic K-Scheduling (MASK), a control architecture designed to sustain robust, risk-aware coordination under strict instantaneous bandwidth caps. We introduce Arbiter-Assisted Semantic Information Gating (A-SIG), a lightweight coordination mechanism that enforces hard access constraints by scheduling only the top-K agents based on locally computed semantic importance scores. By aggregating these prioritized observations into a compact latent state, a self-supervised global encoder enables a distributional policy to mitigate tail risks despite data sparsity. We evaluate MASK across diverse benchmarks, demonstrating that it matches the performance of communication-unconstrained baselines even when channel access is restricted to a small fraction of the swarm size. Furthermore, the framework exhibits inherent resilience to packet erasures, validating semantic scheduling as a critical enabler for resource-constrained 6G systems.

翻译：摘要：实现6G互联机器人愿景需要协调高性能协作控制与物理无线信道严格的频谱限制。在现实协作感知场景中，频谱资源被量化为有限的物理资源块或正交子载波，使得所有智能体同时传输不可行。为此，我们提出多智能体语义K调度（MASK），一种在严格瞬时带宽限制下维持鲁棒、风险感知协调的控制架构。我们引入仲裁辅助语义信息门控（A-SIG），一种轻量级协调机制，通过基于本地计算的语义重要性分数仅调度前K个智能体来强制执行严格接入约束。通过将这些优先观测聚合为紧凑潜在状态，自监督全局编码器使分布策略能够在数据稀疏情况下缓解尾部风险。我们在多种基准测试中评估MASK，证明即使信道接入限制为群体规模的一小部分时，其性能仍可匹配无通信约束的基线。此外，该框架对数据包擦除具有内在鲁棒性，验证了语义调度作为资源受限6G系统关键使能技术的有效性。