Physical AI & Foundation Models: How They Work Together
Physical AI explained: combining foundation models with real-world robot systems. What it means, why it matters, and how to get started.
Defining Physical AI
Physical AI is the application of large-scale AI models — particularly foundation models trained on internet-scale data — to robots and other physical systems that interact with the real world. Unlike traditional robotics (which relies on hand-engineered perception and control) or traditional AI (which operates on text and images), Physical AI bridges both: it uses the broad knowledge of foundation models to enable robots to understand and manipulate the physical world.
Why Now?
Three converging trends make Physical AI viable: (1) Vision-Language-Action models that can directly output robot actions from visual and language inputs; (2) Affordable, capable robot hardware (arms under $10K, humanoids under $20K); (3) Open datasets and open-source models that lower the barrier to entry. RCSV exists at this intersection — providing the hardware, data infrastructure, and knowledge to make Physical AI practical.
How to Get Started
Start with a capable robot (OpenArm, ALOHA), collect 100 demonstrations of a simple task, fine-tune an open VLA model (OpenVLA or Octo), and evaluate in the real world. RCSV offers complete starter kits and guided onboarding for teams new to Physical AI.
Defining Physical AI
Physical AI is the application of large-scale AI models — particularly foundation models trained on internet-scale data — to robots and other physical systems that interact with the real world. Unlike traditional robotics (which relies on hand-engineered perception and control) or traditional AI (which operates on text and images), Physical AI bridges both: it uses the broad knowledge of foundation models to enable robots to understand and manipulate the physical world.
Why Now?
Three converging trends make Physical AI viable: (1) Vision-Language-Action models that can directly output robot actions from visual and language inputs; (2) Affordable, capable robot hardware (arms under $10K, humanoids under $20K); (3) Open datasets and open-source models that lower the barrier to entry. RCSV exists at this intersection — providing the hardware, data infrastructure, and knowledge to make Physical AI practical.
How to Get Started
Start with a capable robot (OpenArm, ALOHA), collect 100 demonstrations of a simple task, fine-tune an open VLA model (OpenVLA or Octo), and evaluate in the real world. RCSV offers complete starter kits and guided onboarding for teams new to Physical AI.