Universal Robots (UR) recently unveiled the UR AI Trainer. Developed in collaboration with Scale AI, the AI Trainer marks a shift as robots move from pre-programmed applications to fully AI-driven tasks. These systems are powered by robust data generated in AI training cells where robots imitate humans.
"Our customers, ranging from large enterprises to AI research labs, are no longer just asking for AI features," said Anders Beck, VP of AI Robotics Products at UR. "They need a way to collect high-fidelity, synchronized robot and vision data to train AI models on the same robots they intend to deploy. Our AI Trainer is the industry's first direct lab-to-factory solution for AI model training."
Alongside the new AI Trainer, UR recently showcased a state-of-the-art robotic foundation model from Generalist AI, a UR preferred model partner. Leveraging this model, two UR robots completed a complex smartphone packaging task, previously impossible without recent advances in the field of Physical AI.
"AI robotics training is often hindered by fragmented hardware and low-fidelity data capture. Much of today's training data is collected on research robots not suited for production environments, and many systems rely only on visual feedback, making delicate or contact-rich tasks difficult. The AI Trainer directly addresses these barriers," said Beck. "By utilizing our unique Direct Torque Control and force feedback features, we give developers direct influence over how the robot physically interacts with the world, training on the same robust hardware used in over 100,000 industrial deployments."
The AI Trainer allows human operators to guide UR robots through tasks in a leader-follower set-up while automatically capturing high-quality multimodal data for robotics AI development. Operators physically guide a "leader" robot through a task while a synchronized "follower" robot mirrors the motion in real time. During each demonstration, the system records synchronized motion, force, and visual data, producing the structured datasets required to train Vision-Language-Action (VLA).
Deploying on UR's AI Accelerator platform, the UR AI Trainer combines UR robots with Scale AI software to enable data capture on UR robots in production and at scale creating continuous feedback that drives ongoing optimization of physical AI systems.
Ben Levin, General Manager, Physical AI at Scale AI, said: "Together with UR, we have created an integrated robotics data flywheel, allowing customers to train, deploy, and improve their AI models faster than ever before."
In one deployment example, two UR3e leader robots provided haptic input to control two UR7e follower robots. The set-up enabled the operator to perform an advanced smartphone packaging task with haptic feedback for imitation learning and VLA training, with demonstration data recorded in real time.
The process of capturing robot training data for AI models was further showcased through a demo that illustrated the same smartphone packaging task, but trained virtually: built in NVIDIA Omniverse and leveraging Isaac Sim, the simulated set-up allowed operators to control a virtual bi-manual UR3e system with real-time haptic feedback using two Haply Inverse3 devices as leaders, providing a physics-accurate simulation.
UR is also exploring the use of the NVIDIA Physical AI Data Factory Blueprint to automate and scale its synthetic data generation, transforming world-scale compute into a production engine for high-quality robotic training data.
For more information contact:
Universal Robots USA
27175 Haggerty Road, Ste. 160
Novi, MI 48377
844-462-6268
sales@universal-robots.com
www.universal-robots.com
Scale AI
303 2nd St., South Tower, 5th Floor
San Francisco, CA 94107
650-294-8644
hello@scale.com
www.scale.com