  In this paper we discuss our experience in teaching the Robotic Applications
Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and
real, heterogeneous, robotic hardware. We discuss the main advantages of our
solutions in terms of seamless simulation-to-real experience for students and
the main shortcomings we encountered with networking and sharing GPUs to
support deep learning workloads. We describe the current and foreseen
alternatives to avoid these drawbacks in future course editions and propose a
more cloud-native approach to deploying multiple robotics applications on a k8s
cluster.
