Cloud-based Digital Twin for Cognitive Robotics
Arthur Nied\'zwiecki, Sascha Jongebloed, Yanxiang Zhan, Michaela, K\"umpel, J\"orn Syrbe, Michael Beetz

TL;DR
This paper introduces a cloud-based digital twin platform for cognitive robotics education, leveraging containerization and web technologies to simplify access, enhance visualization, and facilitate learning and research.
Contribution
The paper presents a novel cloud-based platform using containerization and Kubernetes to deploy robotics simulation and software, improving accessibility and interactivity for cognitive robotics education.
Findings
Platform effectively supports teaching cognitive robotics concepts.
Successful deployment in multiple academic courses.
Enhances accessibility and real-time visualization for learners.
Abstract
The paper presents a novel cloud-based digital twin learning platform for teaching and training concepts of cognitive robotics. Instead of forcing interested learners or students to install a new operating system and bulky, fragile software onto their personal laptops just to solve tutorials or coding assignments of a single lecture on robotics, it would be beneficial to avoid technical setups and directly dive into the content of cognitive robotics. To achieve this, the authors utilize containerization technologies and Kubernetes to deploy and operate containerized applications, including robotics simulation environments and software collections based on the Robot operating System (ROS). The web-based Integrated Development Environment JupyterLab is integrated with RvizWeb and XPRA to provide real-time visualization of sensor data and robot behavior in a user-friendly environment for…
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Taxonomy
TopicsRobotics and Automated Systems · Digital Transformation in Industry
