I am currently pursuing my Ph.D. in the Machine Learning Research Lab at Volkswagen Group and TUM, under the guidance of Prof. Patrick van der Smagt, Dr. Philip Becker-Ehmck and Dr. Maximilian Karl. Prior to joining MLRL, I obtained my bachelor’s and master’s degrees in Mechatronics Engineering from Harbin Institute of Technology. During my studies, I focused on computer vision and worked on projects related to hand pose estimation, generative models, and more. Later on, I became interested in model-based reinforcement learning after reading the World Model paper. I then worked as a Machine Learning Researcher at Polixir Technology, an AI startup in Nanjing, where I primarily focused on offline reinforcement learning for real-world applications.

As an AI enthusiast, I am interested in a broad range of topics within the field. My current research is on the potential of pretrained world model as a foundation model for decision-making. If you would like to discuss these topics or any other related topics, please feel free to contact me at xingyuan.zhang@tum.de.

Looking for postdoc positions.

Research Interests

• World Models
• Imitation Learning
• Reinforcement Learning
• Transfer Learning
• Foundation Models

News

[2025-04-24] Our paper “Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models” has been accepted at TMLR. paper, code, video.

[2025-04-16] Our paper “Recurrent world model with tokenized latent states” has been accepted at ICLR 2025 Workshop on World Models: Understanding, Modelling and Scaling. paper.

[2024-08-12] Our paper “Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models” has been accepted at both CVG workshop @ ICML 2024 and TAFM workshop @ RLC 2024!

[2023-09-22] Our paper “Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models” has been accepted at NeurIPS 2023. Code, datasets and pretrained models are available here. An additional blog post gives a nice intro about the paper.