  We consider the problem of controlling a series of industrial systems, such
as industrial robotics, in a factory environment over a shared wireless channel
leveraging edge computing capabilities. The wireless control system model
supports the offloading of computational intensive functions, such as
perception workloads, to an edge server. However, wireless communications is
prone to packet loss and latency and can lead to instability or task failure if
the link is not kept sufficiently reliable. Because maintaining high
reliability and low latency at all times prohibits scalability due to resource
limitations, we propose a communication-control co-design paradigm that varies
the network quality of service (QoS) and resulting control actions to the
dynamic needs of each plant. We further propose a modular learning framework to
solve the complex learning task without knowledge of plant or communication
models in a series of learning steps and demonstrate its effectiveness in
learning resource-efficient co-design policies in a robotic conveyor belt task.
