Minglun Wei

I am a PhD student at Cardiff University, fully funded by the UK Engineering and Physical Sciences Research Council (EPSRC). I received my Master’s degree from The University of Edinburgh and my Bachelor’s degree from Northwestern Polytechnical University (NWPU). Prior to commencing my PhD, I worked in industry on research projects applying large language models (LLMs) to AI for science.

My research focuses on robotic manipulation of deformable objects in real-world environments, with a particular emphasis on granular materials. This involves developing learning-based or optimisation-based frameworks, with the overall goal of enabling robots to perceive, model, and interact with such complex and highly dynamic materials. I am also interested in data-driven methods for learning surrogate models of dynamical systems.

As a Capricorn (Capricorn sun, Aquarius moon), I enjoy working in a grounded and methodical way while also valuing creativity and innovation in my research. Please feel free to get in touch if you are interested in my work or potential collaborations.

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Education

  • Sep 2016–Aug 2020: BEng in Microelectronics, Northwestern Polytechnical University, Xi’an, China
  • Sep 2020–Aug 2021: MSc in Signal Processing and Communications, The University of Edinburgh, Edinburgh, Scotland
  • Sep 2023–Mar 2027: PhD in Robotics (EPSRC DTP), Cardiff University, Cardiff, Wales

Publications

More in progress...

Celebi paper cover Celebi’s choice: causality-guided skill optimisation for granular manipulation via differentiable simulation
Minglun Wei, Xintong Yang, Junyu Yan, Yu-Kun Lai, Ze Ji
International Conference on Intelligent Robots and Systems (IROS), 2025
paper / pdf / video

We present Celebi, a method that integrates differentiable physics simulation with causal inference to achieve stable and efficient optimisation for robotic excavation and levelling tasks.

Lab paper cover Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation
Minglun Wei, Xintong Yang, Yu-Kun Lai, Ze Ji
Arxiv, 2025
arxiv / pdf

We design a differentiable skill optimisation framework for robotic powder transport, enabling accurate and efficient manipulation in automated laboratory workflows.

AutomaChef paper cover AutomaChef: A Physics-informed Demonstration-guided Learning Framework for Granular Material Manipulation
Minglun Wei, Xintong Yang, Yu-Kun Lai, Seyed Amir Tafrishi, Ze Ji
Arxiv, 2024
arxiv / pdf

We propose a demonstration-guided reinforcement learning framework with a differentiable simulator to enable robotic manipulation of granular materials.

Academic Experience

Reviewer

  • Reviewer for IROS and Autonomous Robots.

Teaching Assistant

  • EN3462/ENT642 Robotics and Computer Vision (final-year undergraduates and postgraduates), 2025
  • ENT633 Artificial Intelligence (postgraduates), 2025
  • EN3062 Robotics and Image Processing (final-year undergraduates), 2024

Research Assistant

  • Research Assistant at Cardiff University, 2025

Scholarships

  • UK EPSRC DTP Studentship, 2023
  • Outstanding Student Scholarship, NWPU, 2019
  • Outstanding Student Scholarship, NWPU, 2018

Template from Jon Barron. Last updated: Oct 2025.