# Cloud Native Robotic Applications with GPU Sharing on Kubernetes

arXiv: https://arxiv.org/abs/2210.03936 · HTML: https://mlsanity.com/paper/2210.03936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [FogROS: An Adaptive Framework for Automating Fog Robotics Deployment](https://mlsanity.com/paper/2108.11355/) (2021)
- [Robotics Software Engineering: A Perspective from the Service Robotics Domain](https://mlsanity.com/paper/2006.10608/) (2020)

## Abstract (arXiv)

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.