Researchers Develop Versatile 3D-Printed Robotic Gripper
A new robotic hand can manipulate items as delicate as eggs or as heavy as a one-kilogram water bottle.
Updated on Sept. 20, 2026 in Robotics
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Engineers from KAIST and Seoul National University of Science and Technology have created a 3D-printed robotic gripper using a highly stretchable material. The hand utilizes pneumatic actuators to mimic human finger movement, allowing it to grasp objects with varying weights and textures.
Why it matters
The development represents a breakthrough in balancing printing precision with the durability required for complex robotic tasks. By automating material discovery, the team aims to accelerate the production of soft robotics for diverse applications.
The robotic gripper can handle objects weighing up to 1 kg and features material capable of stretching sixfold its original length without tearing. The device was manufactured using Digital Light Processing to cure liquid material into complex, functional geometries.
The players
KAIST
The Korea Advanced Institute of Science and Technology is a public research university that focuses on science and technology education.
Seoul National University of Science and Technology
Also known as Seoultech, this institution is a national university in South Korea specializing in engineering and technology research.
The details
The research team employed a machine learning framework trained on chemical formulation datasets to identify the ideal material for the gripper. By combining this AI-driven discovery with Digital Light Processing, they successfully produced a pneumatic actuator that curls like a human finger.
Timeline
September 20, 2026: The research article regarding the robotic gripper was published in Nature Communications.
The Big Picture
This innovation follows the precedent of the soft robotics research track within Nature Communications by bridging material science and machine learning. It shifts the field toward automated discovery, potentially replacing manual trial-and-error methods for creating adaptive polymers.
This technology promises more durable and capable robotic tools for manufacturing, medical implants, and sensor development. Users can expect higher reliability in automation systems as these resilient, 3D-printed materials become integrated into commercial machinery.
The takeaway
The successful integration of machine learning into material science provides a scalable path for creating adaptive, high-performance soft robotics. Industry professionals should monitor how this AI framework influences future manufacturing of sensors and medical devices.
Further reading
Explore more advancements in Robotics on our dedicated technology page.
Source note: This article includes information reported by Globalspec.
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