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Vision-language-action (VLA) models promise a future where a single generalist controller can enable robots to perform a wide range of tasks, from folding laundry and assembling parts to tidying wet labs, without requiring a separate hand-engineered policy for each one.
Despite rapid progress, deploying these models in the real world remains challenging. Many frontier models are closed, making them difficult to reproduce or adapt. Open-weight alternatives are often tied to expensive robot hardware, while reasoning-augmented policies can struggle to meet the latency requirements of closed-loop…
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