Rail research institute develops AI quadruped robot to inspect tracks in place of workers

Development of a railway-specialized robot capable of inspecting track facilities in the field — replacing human workers — is now moving forward in earnest.

The Korea Railroad Research Institute said Monday it has launched a project to develop core technologies for robot-based track inspection. The effort goes beyond existing digital twin systems that monitor railway facilities in virtual space, aiming to enable autonomous inspection robots to operate directly on actual rail lines.

Railway track inspection faces overlapping challenges: worker safety risks, a shortage of data on rare defects, and variability in results depending on inspector skill level. Because actual accidents and defects are uncommon, gathering enough fault cases for AI training is difficult, and repeating hazardous scenarios in real environments is equally impractical.

To overcome these limitations, the institute plans to build a virtual environment. Data collected from actual tracks and their surroundings using drones, cameras and lidar will be used to construct a three-dimensional virtual space, which will then serve as a digital training ground for teaching robots visual perception and movement control.

The virtual environment can replicate the full range of conditions found in the real world — sudden variables such as track intrusions and obstacles, weather conditions including heavy snow and rain, lighting conditions such as nighttime and backlit scenes, and terrain features such as gravel beds and slopes — enabling training across a wide variety of scenarios.

Data learned in the virtual space will be applied to the movements of physical robots, laying the foundation for the locomotion capabilities of a quadruped robot able to traverse both rail surfaces and rough terrain.

The institute expects the technology to shift railway maintenance from a reactive model to a preventive, autonomous one — improving on-site worker safety while strengthening the competitiveness of a Korea-developed autonomous rail inspection solution.

"The performance of an autonomous inspection robot comes down to how accurately it can see and how stably it can move," said Byeon Seong-jun, a senior researcher at the institute. "The core of this research is enhancing the robot's visual perception through a world foundation model, and securing a control policy that allows stable movement even in real track environments through reinforcement learning and Sim2Real technology."