AWS Physical AI Blog

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 […]

The authors would also like to thank Charlie Chang, Amit Kulkarni, Paul Amadeo, Alla Simoneau, Howie Tan for their contributions in making this initiative possible. Introduction Certis is a leading integrated operations service provider, with over 25,000 employees globally and one of the largest Auxiliary Police Forces in Singapore. As part of its broader autonomous […]

Introduction Physical AI development today faces a critical bottleneck. Engineering teams spend disproportionate hours on infrastructure setup, environment configuration, and dependency management—time that could be spent building intelligent robots. The iterative cycle of environment setup, data collection, policy training, and simulation validation consumes weeks of engineering effort before a single real-world test can occur. Even […]

Introduction A customer walks up to an autonomous barista robot at an airport terminal and orders a flat white coffee. The robot has never been explicitly programmed to make one. It knows how to pull espresso shots, steam milk, and control pour volumes, however “flat white” isn’t in its onboard recipe library. Within 300 milliseconds, […]

Self-Driving Labs® are no longer a vision Developing a new drug takes an average of 10-15 years and costs over $2 billion. For patients waiting on life-saving treatments, every month of R&D delay matters. The bottleneck isn’t a lack of scientific talent, it’s the sheer scale of experimentation required. A single drug optimization campaign might […]