LG CNS Holds Robotics & Physical AI Forum, Explores Industrial Applications

About 100 Attend, Including Robotics and AI Faculty and Graduate Students
Discuss Technology Trends and Industrial Applications

LG CNS (LG CNS) said on November 12 that it recently held the 2026 Robotics & Physical AI Forum at its Future Robotics Lab at its Magok headquarters. About 100 people attended, including faculty members and master's and doctoral students from the University of Toronto, Sogang University, Chung-Ang University, and Korea University, as well as LG CNS employees.

At the event, professors in robotics and artificial intelligence (AI), covering fields such as reinforcement learning; robot locomotion and intelligent mechanisms; dynamic and stochastic optimization; machine learning; and deep learning model optimization, exchanged views with LG CNS robotics experts on trends in physical AI technology and ways to apply it in industrial settings.

Faculty members from the University of Toronto, Sogang University, and Chung-Ang University pose for a commemorative photo at the 2026 Robotics & Physical AI Forum hosted by LG CNS. LG CNS

At the forum, participants shared updates on research and demonstrations of core physical AI technologies, including robot foundation models (RFMs), data, intelligent robot hardware, and simulation. They also discussed technical challenges and future directions for applying these technologies in industrial settings.

Drawing on its experience with more than 1,000 robotics-based smart factory and smart logistics projects, LG CNS outlined ways to apply physical AI in industry. It also introduced Physical Works Forge, a robot learning platform launched this year, and Physical Works Baton, an agentic AI-based platform for robot operations and control.

Global researchers shared their latest findings, focusing on robot intelligence and control technologies. Lee Chi-geun, a professor at the University of Toronto, outlined the development of robot foundation models and key technologies for applying them to real-world robots. He explained methods for jointly using data from different types of robots, as well as flow matching, which enables robots to move smoothly and continuously, and action chunking. He also identified differences in robot movements and latency, along with challenges in obtaining data, as issues to be addressed.

Attendees exchanged views on recent advances in physical AI and its applications in industries such as logistics and manufacturing; technical and operational challenges involved in adopting robots; the capabilities companies and universities need to develop; and the role of people in the age of robots.

Lee Jun-ho, executive vice president and head of LG CNS's Smart Logistics & City Business Division, said, "This forum provided an opportunity to share LG CNS's technology and business experience with researchers in robotics and physical AI from Korea and abroad, and to explore together how these technologies can be applied across industries. We will continue to strengthen our technological competitiveness through ongoing engagement with academia and build our business capabilities so that research results can lead to innovation in real-world industrial settings."

This content was produced with the assistance of AI translation services.