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

Chanh Nguyen, Shutong Jin, Florian T. Pokorny, Erik Elmroth

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 $ε$-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.

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

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 -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.
Paper Structure (26 sections, 1 equation, 7 figures, 2 tables, 1 algorithm)

This paper contains 26 sections, 1 equation, 7 figures, 2 tables, 1 algorithm.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Cloud Robotics Platforms
  4. Policy Learning and Sequence Prediction
  5. Latency-Resilient Control and Runtime Verification
  6. PROBLEM FORMULATION
  7. Continuous Manipulation as a Markov Decision Process
  8. Cloud-Induced Delay and Open-Loop Vulnerability
  9. SPECULATIVE POLICY ORCHESTRATION
  10. Cloud-Side Autoregressive Generation
  11. Adaptive Horizon Scaling (AHS)
  12. Continuous-State $\epsilon$-Tube Verification
  13. EXPERIMENTS
  14. Experimental Setup
  15. Task Environments and MDP Configuration
  16. ...and 11 more sections

Figures (7)

  • Figure 1: Comparison of traditional cloud robotics architectures with the proposed SPO framework. (a) Traditional deployments are bottlenecked by network latency, forcing them to rely on discrete macro-goals or expensive local compute. (b) SPO leverages a centralized World Model to proactively serve high-frequency, continuous kinematic chunks to a fleet of edge robots, enforcing kinematic safety limits via local $\epsilon$-tube verifiers.
  • Figure 2: Architecture of SPO. The cloud executes computationally intensive speculative rollouts, adaptively scaled by the AHS module, while the edge enforces safety constraints using a lightweight $\epsilon$-tube verifier.
  • Figure 3: RLBench tasks used in the evaluation. From left to right, the tasks represent increasing levels of manipulation complexity.
  • Figure 4: Distributed experimental setup where the edge runs RLBench and verification, while the cloud hosts the world model and policy.
  • Figure 5: Adaptive Horizon Scaling Dynamics.
  • ...and 2 more figures