AWS Releases Guide for Building Physical AI Training Pipelines Using SageMaker and IoT Greengrass

AWS has released a detailed guide for building Physical AI training pipelines. Given that simulation-based learning (Sim-to-Real) is essential to reduce the time, cost, and risk of damage in real robot training, the guide analyzes the differences in hardware requirements across three computing environments—training, simulation, and edge deployment—and proposes optimal infrastructure configurations.

Training Environment and Infrastructure Configuration

For one-off fine-tuning, SageMaker Training Job is recommended to minimize costs via automatic instance termination. For long-term iterative experiments lasting days to weeks, SageMaker HyperPod is recommended to maintain a persistent environment. For simulation workstations, EC2 G6e (L40S GPU) with RTX cores required for Isaac Sim rendering is used, separated from training GPUs (A100/H100) to improve cost efficiency. Amazon S3 and FSx for Lustre are utilized as hubs for data storage, modularly connecting the training, evaluation, and deployment pipelines.

VLA and RL Track Implementation

In the VLA track, NVIDIA GR00T N1.x models are fine-tuned using the SO-101 robot arm dataset. SageMaker Pipelines automate the process from data transformation to model registration, while closed-loop evaluation is performed in Isaac Lab on the workstation. The RL track trains using the PPO algorithm on a HyperPod Slurm cluster, achieving a performance of approximately 40,000 steps per second by simultaneously simulating 2,048 environments.

Edge Deployment and Optimization

Trained models are deployed to edge devices such as NVIDIA Jetson via AWS IoT Greengrass. Applying TensorRT optimization yields latency improvements of 2.1x on servers (L40S) and 1.26x on edge devices (Thor) compared to PyTorch. The Greengrass component structure simplifies deployment during model replacement by requiring only configuration changes, while security is enhanced through X.509 certificates and Token Exchange Roles.