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Nvidia’s DrEureka outperforms humans in training robotics systems
- Large language models (LLMs) can accelerate the training of robotics systems in super-human ways according to researchers at Nvidia, University of Pennsylvania, and the University of Texas.
- The study introduces DrEureka, a technique that creates reward functions and randomisation distributions for robotics systems with a high-level task description.
- DrEureka is a faster and more efficient solution for transferring learned policies from simulated environments to the real world.
- LLMs can combine their vast world knowledge and reasoning capabilities with the physics engines of virtual simulators to learn complex low-level skills.
- DrEureka uses a language-model driven pipeline for sim-to-real transfer with minimal human intervention.
- Policies trained using DrEureka outperform the classic human-designed systems for quadruped locomotion by 34% and 20% in forward velocity and distance travelled, across various real-world evaluation terrains.
- Best policy trained by DrEureka for robotic hands performed 300% more cube rotations than human-developed policies.
- The safety instruction included in the task description plays an important role in ensuring that the LLM generates logical instructions that transfer to the real world.
- DrEureka demonstrates the potential of accelerating robot learning research by using foundation models to automate the difficult design aspects of low-level skill learning.
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