AWS Physical AI Blog

Introduction Figure 1: Geometric intelligence visualization showing 3D CAD wireframe parts connected by AI matching arcs Every original equipment manufacturer faces the same invisible cost. An engineer designs a component, unaware that a functionally identical part already sits in a warehouse at another facility, cataloged under a different name in a different system. A procurement […]

Introduction Digital twins shift manufacturing teams from reactive maintenance to predictive operations. A production-grade twin goes beyond static 3D. It ingests live data, predicts failures, and self-corrects as equipment ages. In this post, you’ll learn how to architect a multi-level digital twin on AWS using Universal Scene Description (OpenUSD) for scene composition and Spatial Data […]

Introduction Edge intelligence is transforming manufacturing and logistics. Cameras and sensors now instrument every zone of a facility: loading docks, production lines, staging areas, aisles between racking. These devices produce continuous streams of operational data, yet most of it flows into storage without producing a timely, actionable answer. A warehouse operator looking for a specific […]

Config Intelligence Inc. (Config) is building the data infrastructure and technology necessary to realize General-Purpose Robot Foundation Models. They operate an end-to-end pipeline that spans from large-scale data collection and preprocessing to model training and real-world deployment, enabling robots to perform bimanual manipulation tasks reliably across diverse environments. To date, they have accumulated more than […]

Co-authored by AWS, Galeo Tech, and Multiverse Computing Introduction Physical AI, the class of systems that perceive, reason, and act in the real world, has a familiar progression. The initial prototype works and showcases a lot of promise. The second prototype extends functionality to a different device. The harder question comes next: how do we […]

Amazon Web Services would like to thank the UneeQ Engineering team for their contribution to this post Introduction UneeQ creates AI-powered digital humans that engage in natural conversations while displaying realistic facial expressions through real-time high-fidelity 3D rendering. Running these compute-intensive workloads, built on game engines like Unreal Engine, is inherently complex. That complexity multiplies […]

Introduction Fine-tuning a Vision-Language-Action (VLA) model like OpenVLA with LoRA on Amazon SageMaker AI lets you adapt a 7-billion parameter robot brain to a new task in hours, not days. This cuts GPU compute costs, shortens adaptation cycles, and lets Physical AI engineers focus on their core domain rather than on infrastructure. Physical AI, a […]

For robots to see, understand, and physically handle objects in human-centered work environments, they need to learn from real operational settings, not just controlled lab demonstrations. RLWRLD, a Physical AI company founded in 2024, is building RLDX, a robotics foundation model designed to train on real-world industrial data and enable robots to perform dexterous manipulation […]

A different recipe for training robot world models: compose pre-trained AI modules with classical computer vision to extract scene semantics from ordinary monocular video — no domain data, no synthetic frames. Introduction Today’s recipe for training robot AI looks the same almost everywhere: feed a giant neural network billions of pixels paired with text instructions […]

Introduction Manufacturers need automation that adapts to changing products without costly rework. At Hannover Messe 2026, SoftServe and AWS demonstrated a simulation-first approach to flexible robotic manufacturing, powered by AWS cloud services for AI orchestration, IoT communication, and quality inspection that ran continuously for five days with a near-100 percent pick success rate during live […]