KIMM Park Cheol-hoon, principal researcher at the Advanced Robotics Research Center, AI Robot Research Institute, develops an automatic loom for "muscle fabric" and the produced muscle fabric. /Courtesy of KIMM

A domestic research team has developed equipment that can automatically and continuously produce artificial muscle yarns thinner than human hair like fabric. Using this, light and flexible "muscle fabric" can be mass-produced, accelerating the commercialization of wearable robots that you wear and move in.

The research team led by Principal Researcher Park Cheol-hun at the Advanced Robotics Research Center of the Artificial Intelligence (AI) Robotics Research Institute of the Korea Institute of Machinery & Materials (KIMM) said on the 29th that it developed an "automatic loom" that automatically weaves muscle fabric.

The equipment makes very thin shape memory alloy yarns, about one-quarter the thickness of a human hair, into coils and weaves them like fabric. The resulting muscle fabric is powerful enough to lift 10–15 kg with a weight of 10 g, making it suitable as a core power source for wearable robots.

Previously, automatic weaving was difficult because of coil yarns with a metal core, but the team solved the problem by placing natural yarn at the center and changing the equipment structure and manufacturing process. As a result, muscle fabric can now be mass-produced continuously without breaks.

Using this technology, the team built a robot weighing under 2 kg that simultaneously assists three joints—the elbow, shoulder, and waist—and, in tests on patients with muscle weakness, improved the shoulder range of motion by more than 57%.

Park said, "By securing technology to automatically mass-produce muscle fabric, we can rapidly commercialize wearable robots in various fields such as healthcare, logistics, and construction."

The research results were published on Oct. 24 in the international journal TNSRE (IEEE Transactions on Neural Systems and Rehabilitation Engineering).

References

IEEE Transactions on Neural Systems and Rehabilitation Engineering (2025), DOI: doi.org/10.1109/TNSRE.2025.3613709

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