Scale Robot Reinforcement Learning with NVIDIA Isaac Lab on Amazon SageMaker AI | Amazon Web Services
Physical AI is moving from research into production. Robots are increasingly trained in high-fidelity simulation before being deployed to factories, warehouses, and logistics centers, because training in the real world is slow, expensive, and often unsafe, while GPU-accelerated simulation can compress months of learning into hours.
This shifts the challenge to compute. Reinforcement learning (RL) for complex behaviors like humanoid locomotion on rough terrain is compute-intensive, with single-node training runs stretching from hours to days. Robotics teams need to iterate quickly during research and also run production-grade, long-horizon training jobs without the operational burden of maintaining compute clusters.
In this post, we show how to train robot policies for the Unitree H1 humanoid with NVIDIA Isaac Lab on Amazon SageMaker AI across two compute options: Amazon SageMaker HyperPod and Amazon SageMaker Training Jobs. The full code of this solution is available in the accompanying GitHub repository.
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