Nvidia’s DrEureka outperforms humans in training robotics systems

VentureBeat May 6, 2024
Ben Dickson

Large language models (LLMs) can accelerate the training of robotics systems in super-human ways, according to a new study by scientists at Nvidia, the University of Pennsylvania and the University of Texas, Austin.

The study introduces DrEureka, a technique that can automatically create reward functions and randomization distributions for robotics systems. DrEureka stands for Domain Randomization Eureka. DrEureka only requires a high-level description of the target task and is faster and more efficient than human-designed rewards in transferring learned policies from simulated environments to the real world.

The implications can be great for the fast-moving world of robotics, which has recently gotten a renewed boost from the advances in language and vision models.

Sim-to-real transfer

When designing robotics models for...

Today's Sponsors

Today's Sponsor