Profiling- and learning-based co-design of communication and compute in scalable robotics

Abstract

The proposal of Cloud/Fog Automation introduces a new architecture for industrial automation, which breaks the boundaries between information technology, operational technology, and communication technology domains, facilitating information sharing and optimizing the system as a whole. This paper extends the vision of Cloud/Fog Automation to scalable robotics, which is deployed across the device-edge-cloud continuum using cloud-native technologies and calls for a co-design methodology that jointly considers communication, compute, and application characteristics. From an industrial practitioner’s viewpoint, we demonstrate its feasibility with a comprehensive approach to optimizing a mobile robot application. We leverage a dual-phase optimization strategy: static optimization pre-deployment and dynamic optimization post-deployment. In the static optimization phase, we employ a profiling-based method to minimize communication overhead while balancing computational load. The dynamic optimization phase utilizes a reinforcement learning-based approach to explore an optimal policy for computation offloading and network quality of service configuration to maximize edge server utilization and lower network usage costs while guaranteeing application performance. Experimental results, validated through a Simulation-to-Reality (Sim-to-Real) approach, demonstrate that our co-design method significantly enhances operational efficiency, reduces network costs, and improves overall system responsiveness.