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- Guidance for Physical AI for Robotics on AWS
Guidance for Physical AI for Robotics on AWS
Overview
This Guidance demonstrates how to integrate advanced AI capabilities with physical robotics systems on AWS, enabling autonomous operation in real-world environments. It helps organizations implement end-to-end solutions for developing and training robots with AI-based autonomy, combining deep learning, large language models, and reinforcement learning with physical sensing and control systems. The solution shows both robotics OEMs and industrial users how to create adaptive, self-learning robots that can perceive, reason, and interact autonomously in operational settings. Furthermore, it demonstrates how to leverage AWS's scalable infrastructure for continuous simulation, training, and improvement of robotic systems that evolve to meet changing industrial needs.
Benefits
Enable seamless transition of Physical AI virtual training to real-world deployment while continuing the training in real-world environments modeled by intelligent sensors to meet target autonomous robot application requirements.
Accelerate development by choosing between simulation with NVIDIA's physics engine or real-world training with Amazon SageMaker's ML capabilities, and combining both paths for comprehensive Physical AI robot applications development.
Enable continuous robot evolution with AWS IoT Greengrass edge deployment that serves a dual purpose - executing ML inference for real-time robot control while simultaneously collecting sensor data for ongoing model improvement and adaptation.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
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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