Nvidia unveils ENPIRE: self-training robots reach 99% success

Nvidia has unveiled ENPIRE, a research system built with Carnegie Mellon University and the University of California, Berkeley. The idea is simple enough: train robots on real hardware instead of only in simulation, and do it with very little human involvement.

Nvidia says ENPIRE runs a fully autonomous loop from start to finish. It resets the workspace, tries a strategy, checks the result, and updates its policy.

In the company’s testing, eight robots reached success rates as high as 99% on manipulation tasks that would normally need manual resets, hand-built reward functions, and close engineering oversight.

Those tests covered Push-T, sorting pins into a box, cutting or fastening cable ties, pin insertion, and assembly-style work like installing a GPU into a motherboard.

Nvidia also says ENPIRE can generate reward functions and success checks from only a few minutes of success-and-failure video. It does that by using visual alignment, gripper height, estimated force, and views from two cameras, while keeping response times under 150 ms.

If you follow robotics, this one deserves a look.

After a lightly supervised initial setup, Nvidia says eight dual-arm YAM stations can share code through Git, read papers, test hypotheses, and decide whether behavior cloning or reinforcement learning makes more sense.

In one example, Nvidia says ENPIRE reached 100% pin insertion faster than a human-in-the-loop baseline. The company says ENPIRE builds on Nvidia’s Eureka, which beat human-written rewards on more than 80% of tasks, with gains above 50%, and Nvidia plans to open-source ENPIRE alongside Project GR00T and Nvidia Isaac.