AWS Physical AI Blog

Physical AI is built in simulation and digital twins before it reaches the real world. Engineers validate a factory layout, inspect a robot work cell, or teleoperate a robot to collect training demonstrations, and these tasks are often best done from inside a 3D scene itself. The challenge is that photorealistic, physics-accurate scenes need workstation-class […]

Sequel to the VAMS 2.0 Release post (20 June 2024). AWS Physical AI and Prototyping and AI Customer Engineering (PACE) are excited to announce the release of the Visual Asset Management System (VAMS) 2.6, the most substantial update to the open-source accelerator solution since we introduced the platform’s modern foundation two years ago. Where VAMS […]

Industrial environments generate vast amounts of sensor data, but most of it remains underutilized. Deployed on AWS, Archetype AI’s Newton physical world model brings Physical AI to manufacturing, turning raw signals into real-time operational intelligence. Introduction The next wave of AI is moving beyond the digital realm and into the physical world. Foundation models have […]

The second in a series exploring how AWS and RLWRLD are advancing Physical AI from research to real-world deployment, and why dexterous robotics isn’t about replacing workers but sustaining economies that are running out of them. Building on a Foundation In June 2026, we published Putting Dexterous Robots to Work: How RLWRLD Builds Physical AI […]

End-to-End solution for Training and Deploying Physical AI Policies on AWS at Scale Physical AI is artificial intelligence that operates in the real world through robots, autonomous vehicles, and smart factories. According to the Capgemini Research Institute, 79% of organizations are already engaging with Physical AI, and 60% of executives believe that physical AI will […]

Gaussian Splatting is dramatically lowering the cost and complexity of photorealistic 3D, making once impractical use cases like near real time capture, robotics, and simulation viable across media, construction, manufacturing, and real estate. AWS Physical AI | Blog #2 in the 3D Gaussian Splatting Series From Research Breakthrough to Business Advantage When we introduced 3D […]

Introduction: The missing layer in Physical AI on AWS Physical AI systems, those that perceive, reason, and act in the physical world, are moving fast from research into production deployments. Training policies in GPU-accelerated simulation compress months of real-world experience into hours of computing, without the cost, time, or safety risks of physical experimentation. A […]

Introduction In our previous blog, we demonstrated how NVIDIA Cosmos world foundation models on AWS address the data scarcity problem in physical AI: the challenge of training robust perception, prediction, and control models when real-world robot and sensor data is expensive, unsafe, or impractical to collect at scale. We introduced two production-ready architectures: Cosmos NIM […]

Introduction This post demonstrates how to fine-tune π0 (Pi-Zero), a 3-billion parameter flow matching Vision-Language-Action model from Physical Intelligence, on robot manipulation datasets using Amazon SageMaker HyperPod with Amazon Elastic Kubernetes Service (Amazon EKS) orchestration. Two standard robotics benchmarks provide the training data: DROID for real-world manipulation and LIBERO-10 for simulated tabletop tasks, with a […]

Introduction Imagine walking into a convenience store in Tokyo and watching a robot scan your items, bag your groceries, and restock the shelves — all without missing a beat. That’s not a vision of the future, it’s what happens when a world model running on AWS gives a robot the ability to imagine before it […]