Computer Vision · Embodied AI · Robotics
Feng Yan
Ph.D. candidate and researcher in embodied intelligence, with interests in multimodal large models, vision-language-action systems, world models, robotic manipulation, autonomous systems, perception, and tracking.
Research Interests
Learning generalizable perception and action representations.
Open-world Vision
Foundation models for open-vocabulary segmentation, detection, tracking, and compositional visual understanding.
Embodied AI
Vision-language-action models, world models, and Sim2Real learning for robotic manipulation and embodied decision making.
Autonomous Systems
Multimodal models for perception, prediction, planning, and generalization in autonomous driving and navigation.
Efficient AI Systems
Data-centric training, model compression, edge inference, and evaluation protocols for deployable embodied intelligence.
Experience & Education
Academic training and applied research experience.
Embodied AI and multimodal perception
Feng's research and engineering experience spans embodied AI, computer vision, multimodal systems, digital twins, and data-driven perception systems across enacta ai, Meituan, and ArcSoft.
Generalization, efficiency, and real-world grounding
His work studies robotic manipulation, VLA models, world models, multi-camera perception, mobile vision, model compression, simulation, and evaluation of embodied intelligence systems.
Xi'an Jiaotong University
Ph.D. candidate in embodied intelligence at Xi'an Jiaotong University. M.S. from Xi'an Jiaotong University.
University of Electronic Science and Technology of China
B.S. in Automation from UESTC, with an early focus on intelligent systems, robotics, and control.
Selected Work
Publications
Contact
Contact
For research collaboration, academic discussion, and the latest publication information, please use email or visit the Google Scholar profile.