Embodied AI Blog Series, Part 1: Getting Started with Robot Learning on AWS Batch

This article introduces a scalable AWS infrastructure for fine-tuning NVIDIA Isaac GR00T N1.5, a foundation model for robot learning, enabling teams to accelerate development cycles for physical AI applications.

• Robot learning shifts from model-based control to data-driven paradigms for autonomous systems
• GR00T N1.5 3B reduces training time from months to hours with fewer demonstrations
• Architecture combines AWS Batch, Amazon EFS, Amazon ECR, and Amazon DCV for scalable fine-tuning
• AWS CDK automates infrastructure provisioning for repeatable, cost-efficient training pipelines
• Real-time monitoring via TensorBoard and simulation evaluation in Isaac Lab
• Supports both simulated and physical SO-ARM101 robot evaluation
• Open-source LeRobot framework democratizes robot learning stack

This solution enables teams to rapidly iterate on robot policies by combining cloud elasticity with NVIDIA's advanced robot learning stack, reducing operational complexity for physical AI development.