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Xilab-Robotics is a research laboratory at the Department of Sciences and Methods for Engineering" of the University of Modena and Reggio Emilia (UNIMORE), Italy, specialized in the integrated design of industrial products and highly automated production systems.
XiLab researchers are experts in CAD/CAE design, advanced simulation, industrial robotics, automation, and mechatronic design. Our work bridges fundamental research and industrial innovation, contributing to both academic advancement and real-world technological impact.
🌐 Official Website: https://www.xilab.unimore.it/
Our mission is to:
We aim to create intelligent robotic systems that are precise, efficient, robust, adaptive, and capable of operating safely in complex and dynamic environments.
This .github repository is used to manage the public profile of the Xilab-Robotics GitHub organization.
It may contain:
📧 Prof. Marcello Pellicciari marcello.pellicciari@unimore.it
📧 Prof. Roberto Raffaeli: roberto.raffaeli@unimore.it
📧 Prof. Pietro Bilancia: pietro.bilancia@unimore.it
🏫 Department of Sciences and Methods for Engineering (DISMI)
🎓 University of Modena and Reggio Emilia (UNIMORE)
For more information about our projects, publications, and collaborations, please visit our official website.
If you are a student, researcher, or industry partner interested in robotics and intelligent systems, explore our repositories and feel free to connect with us.
We welcome collaborations, research proposals, and open innovation initiatives.
Implementation of Physics-Informed Neural Networks (PINNs) for Rotational Transmission Error Compensation in RV Reducers
This repository holds the third generation of the Mixed Reality Toolkit for Unity. The latest version of the MRTK can be found here.
Graphics tools and components for developing Mixed Reality applications in Unity.
This dataset refers to the paper titled "An integrated engineering approach for the preliminary design and synthesis of delta robots" published on Springer IJIDeM journal (https://doi.org/10.1007/s12008-025-02443-y).
This dataset refers to the paper titled "Enabling Manual Guidance in High-Payload Industrial Robots for Flexible Manufacturing Applications in Large Workspaces" published on MDPI Machines journal (https://doi.org/10.3390/machines13111016).
This organization has no public members. You must be a member to see who’s a part of this organization.
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