A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-augmented ICPS
Top-5 Most-Cited Paper in IEEE TICPS · 78+ citations · ICPS-to-AI-augmented framework
Industrial Physical AI Researcher
I bring Physical AI to the industrial floor — translating field constraints into embodied systems that actually run on commodity sensors and existing ROS 2 stacks.
Research focus on Industrial AMR Navigation, Low-cost Sensing, LLM/VLA-Driven Operations, and Physical AI deployment.
A research direction that brings Physical AI down to the industrial floor — across the two embodied platforms that drive modern manufacturing: the Industrial AMR that moves, and the Manipulator that acts. Both run on commodity sensors, legacy ROS 2 stacks, and the same operational metrics. My research builds the AI that ships across both.
Mobile platforms that move across the floor.
Articulated arms that act on the world.
Both platforms live on the same operational floor, and both ask the same question: can we make embodied intelligence actually deployable? Industrial Physical AI is my answer.
Curated from 13+ international peer-reviewed papers (8 first or co-first authored). Featured items highlight the most representative threads of recent research.
Top-5 Most-Cited Paper in IEEE TICPS · 78+ citations · ICPS-to-AI-augmented framework
Highest IF Among Industrial Engineering Journals · JCR Q1 · Cooperative Multi-AMR navigation
Patent Filed: Indoor Map Generation (KR 10-2024-0145250 + PCT) · Low-cost 2D LiDAR glass detection
First systematic framework defining Cyber-Physical AI · Foundation for Industrial Physical AI direction
Survey of Physical AI in Industrial Robotics · Bridging hype and deployment reality
First LLM-driven costmap framework for industrial AMRs · Patent Filed (KR 10-2025-0213292)
13+ international papers · 8 first or co-first · 1 domestic · 5 patents.
* indicates equal contribution as first authors.