Xinyu Chen

Towards the Accelerator Metaverse: Spatial Computing Interfaces for Crossmodal Human-Robot Collaboration in Accelerators
Supervisors: Prof. Wim Leemans (DESY, UHH), Prof. Frank Steinicke (UHH), Dr. Susanne Schmidt (UHH)

Xinyu Chen received her Bachelor’s degree in Mechatronics from Tongji University (Shanghai, China), followed by a Master’s degree in Robotics, Cognition, Intelligence from Technical University of Munich (Munich, Germany). During her Master’s studies, she worked for two years as a Junior Machine Learning Engineer in the AI-Industrialization at Bosch Reseach (Renningen, Germany). Her Master’s thesis, Learning Robot Workspace Representation with Neural Fields, proposed a neural network-based method for generating data-intensive robot kinematic maps within milliseconds. The preliminary results were presented at the Embodiment-Aware Robot Learning (EARL) workshop of Robotics: Science and Systems (R:SS) 2024.

Her research interests lie in multi-modal human-robot interaction, integrating artificial intelligence, mixed reality, and robotics. The aim of her project is to develop a cross-modal interface that enables human operators to teleoperate robots deployed in particle accelerators. This system is intended to facilitate machine maintenance without requiring shutdowns, while minimizing radiation exposure risks for human workers.