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AWS Solutions Library

Guidance for AI-Driven Robotic Simulation and Training on AWS

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Overview

This Guidance demonstrates how to build an AI-assisted robot training and fleet management system using Amazon Bedrock foundation models and AWS Trainium. It helps organizations overcome the complexity of training robots for precise tasks and managing fleets at scale through two complementary methodologies: imitation learning using NVIDIA Isaac Sim on Amazon EC2, and reinforcement learning with Bedrock-generated reward functions. The solution accelerates training with AWS Trainium, standardizes data processing with LeRobot datasets, and enables seamless fleet deployment through AWS IoT Core. This comprehensive approach reduces implementation time, ensures scalability, and delivers robust industrial robotics capabilities without requiring deep AI expertise.

Benefits

Deploy simulation environments and train robotic models faster with Amazon EKS, AWS Trainium, and NVIDIA Isaac Sim. Reduce development cycles from months to days by running multiple parallel simulations while leveraging purpose-built ML acceleration.

Integrate foundation models from Amazon Bedrock to analyze workspace conditions and generate optimal robot strategies. Your robots can make more intelligent decisions by combining the reasoning capabilities of LLMs with precision ML algorithms for complex manipulation tasks.

Automate the distribution of trained models to your robot fleet using AWS IoT Core and IoT Device Management. Focus on improving robot performance while AWS services handle secure model deployment, monitoring, and management across your entire fleet.

How it works

Imitation Learning Simulation Environment

This architecture diagram shows a robotic learning system integrating the intelligence of foundation models with ML and mathematical algorithms, accelerated by AWS Trainium/GPU infrastructure and managed through cloud-native technologies.

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Reinforcement Learning Training Environment

This architecture diagram shows developers how to train robotic agents using NVIDIA Isaac Sim on Amazon EKS with LLM-generated reward functions, then automatically deploy trained models to physical robots using AWS IoT services.

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Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs. 

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Related content

How to expansively train Robot Learning by Customers on AWS using functions generated by Large Language Models

This blog post demonstrates how to implement scalable robot learning on AWS using Large Language Models for generating reward functions, leveraging services like Amazon EKS, Amazon FSx, and NICE DCV for distributed training and visualization of robotic simulations.

Learn more

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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