Allonic transforms robotics manufacturing with 3D Tissue Braiding

Hungarian startup Allonic Ltd. is developing a highly innovative process for manufacturing complex, compliant robotics that leverages 3D printing and its own technology, 3D Tissue Braiding. In recognition of this work, the young company has been selected as a finalist in the Startup category of the Formnext Awards 2025.

At the foundation of the company’s work is the goal to develop advanced robots that more closely mimic natural movements and can replicate the delicate, natural gestures of human bodies without dramatically driving up manufacturing complexity and costs. The solution Allonic has proposed, 3D Tissue Braiding, seems highly promising on this front, as demonstrated in a robotic hand it has made.

3D Tissue Braiding is a technology that, as Allonic puts it, “enables the fully automated growth of complex, movable robot bodies directly from functional fabrics.” The idea, in more precise terms, is that users can 3D print a base for the robot (i.e. the bones) and then leverage Allonic’s automated hardware and software solution to robotically weave fibers around the pre-manufactured components. The design of the weave is critical to the function of the robot, as it integrates not only reinforcing textiles with uniform stress distribution, but also pulleys and guides to be attached to actuators for naturalistic movements. It’s hard not to notice that the 3D braided fibers look like muscles and tendons around the bones of the hand.

The 3D Tissue Braiding solution brings with it a host of benefits, including faster prototyping cycles for complex robotics—from weeks to hours; lower production costs unlocked by the streamlined, automated platform; and more nimble robotics that are soft, strong and human-safe. The process can also make multi-material 3D structures with integrated elastics, sensing and wiring for enhanced functionality. At this stage in the technology’s development, Allonic has developed a second-generation system that is reportedly five times faster, two times smaller and has three times larger resolution for yarn count than the previous iteration.