R&D and Education
Flexiv remains dedicated to empowering the next generation of AI researchers and roboticists. We achieve this by offering powerful and user-friendly programming interfaces, enabling them to concentrate on their fundamental research and effortlessly showcase their experimental outcomes.
Contact UsRelied on by leading universities and research facilities
Grasping without squeezing
Stanford's researchers have pioneered a groundbreaking gripper, harnessing the power of hybrid electrostatic adhesives. This innovative gripper excels at generating substantial shear forces, even in situations where minimal normal forces are at play.
When combined with Flexiv's precision force control technology, this gripper allows for the secure handling of both sizable and fragile objects without the need for excessive pressure.
Robots learn to grasp any object
Effective object grasping is essential in numerous applications, and deep learning techniques hold great potential for enhancing this capability. However, as with many learning-based approaches, the creation of realistic and diverse training data is a crucial necessity. Addressing this challenge, researchers at Shanghai Jiao Tong University have introduced a large scale grasp pose detection dataset.
The resulting systems, trained on this dataset, underwent evaluation using Flexiv's robot arm and demonstrated impressive state-of-the-art performance.
Elevate your R&D projects with the power of adaptive robotics.
Real-time safe guidance for medical operations
Human-robot collaboration in medical operations promises enhanced efficiency and performance. It's crucial to underscore that safety remains paramount when deploying robotic devices in such contexts.
The ZJU-UIUC Institute has developed a dual Rizon robot platform tailored for surgical procedures. This system uses vision and force feedback to offer guidance to medical personnel, and protects patients from harm through the implementation of force-based protective barriers.
Reactive grasping of dynamic targets
Reactive grasping, the ability to intelligently grasp objects in motion, finds extensive industrial applications, such as packing items into boxes on a conveyor belt and performing tasks while the robot is moving with an AMR. To develop this innovative capability, Shanghai Jiao Tong University has created a neural network system that tracks inferred grasps during the motion of the target object, building upon existing grasp pose detection algorithms.
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