Speculative Policy Orchestration: A Latency-Resilient Framework for Cloud-Robotic Manipulation

TL;DR

This paper introduces Speculative Policy Orchestration, a latency-resilient framework for cloud-robotic manipulation that pre-computes future commands and adaptively manages prediction errors to ensure safe, real-time control despite network delays.

Contribution

The paper presents a novel framework combining speculative pre-computation, error bounding, and adaptive horizon scaling to improve cloud-robotic manipulation under network latency.

Findings

Reduces network-induced idle time by over 60%.

Discards 60% fewer cloud predictions than static baselines.

Enables fluid, real-time control with safety guarantees.

Abstract

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 $\epsilon$-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.