The Architecture Is Embarrassingly Simple
Most robotics papers make VLA (Vision-Language-Action) models sound like some radical new paradigm. They're not. RT-2 from Google DeepMind and OpenVLA from Stanford are just vision transformers with action tokens bolted onto the output vocabulary. That's it. The "revolutionary" insight is treating robot actions as language tokens — which, in hindsight, seems almost obvious.
I've been digging through the RT-2 codebase and OpenVLA's implementation, and here's what surprised me: the core inference loop is maybe 50 lines of meaningful code. The rest is tokenization gymnastics.
Why Roboticists Overcomplicate This
The traditional robotics stack looks something like: perception module → state estimator → planner → controller → actuator commands. Each component has its own failure modes, latencies, and PhD-level complexity. VLA models collapse all of that into a single forward pass through a transformer.
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