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Nvidia’s DrEureka outperforms humans in training robotics systems
DrEureka takes a robotic task description and uses an LLM to generate software implementations for a reward function that measures success in that task.
Don’t miss out on the chance to gain insights from industry experts, network with like-minded innovators, and explore the future of GenAI with customer experiences and optimize business processes. Request an invite Recent works have shown that LLMs can combine their vast world knowledge and reasoning capabilities with the physics engines of virtual simulators to learn complex low-level skills. Their findings show that in quadruped locomotion, policies trained with DrEureka outperform the classic human-designed systems by 34% in forward velocity and 20% in distance traveled across various real-world evaluation terrains.
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