Cloud robotics enables robots to offload high-dimensional motion planning and reasoning to remote servers. However, for continuous manipulation tasks requiring high-frequency control, network latency and jitter can severely destabilize the system, causing command starvation and unsafe physical execution. To address this, we propose Speculative Policy Orchestration (SPO), a latency-resilient cloud-edge framework. SPO utilizes a cloud-hosted world model to pre-compute and stream future kinematic waypoints to a local edge buffer, decoupling execution frequency from network round-trip time. To mitigate unsafe execution caused by predictive drift, the edge node employs an $ε$-tube verifier that strictly bounds kinematic execution errors. The framework is coupled with an Adaptive Horizon Scaling mechanism that dynamically expands or shrinks the speculative pre-fetch depth based on real-time tracking error. We evaluate SPO on continuous RLBench manipulation tasks under emulated network delays. Results show that even when deployed with learned models of modest accuracy, SPO reduces network-induced idle time by over 60% compared to blocking remote inference. Furthermore, SPO discards approximately 60% fewer cloud predictions than static caching baselines. Ultimately, SPO enables fluid, real-time cloud-robotic control while maintaining bounded physical safety.

翻译：云机器人技术使机器人能够将高维运动规划与推理任务卸载至远程服务器。然而，对于需要高频控制的连续操作任务而言，网络延迟和抖动会严重破坏系统稳定性，导致指令匮乏及不安全的物理执行。针对此问题，我们提出投机策略编排（SPO），一种延迟弹性的云边协同框架。SPO利用云端世界模型预计算并流式传输未来运动学路径点至本地边缘缓冲区，从而将执行频率与网络往返时间解耦。为缓解预测漂移导致的不安全执行，边缘节点采用ε-管验证器严格约束运动学执行误差。该框架集成自适应视界缩放机制，可根据实时跟踪误差动态扩展或收缩投机预取深度。我们在模拟网络延迟条件下，对连续RLBench操作任务评估SPO性能。结果表明，即便部署中等精度的学习模型，SPO相比阻塞式远程推理可减少60%以上的网络诱发空闲时间。此外，与静态缓存基线相比，SPO丢弃的云端预测数量减少约60%。最终，SPO在维持物理安全边界的前提下，实现了流畅的实时云机器人控制。