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Latent.Space interview explains world-action models for robot control

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Latent.Space announces a podcast episode with Dyna Robotics' Jason Ma, who explains how world-action models predict the future to guide robot actions. The episode also covers training on over a million hours of human video, which lets robots adapt to new tasks with as little as 45 minutes of demonstrations.

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Robots That Predict the Future: Dyna-2, Million-Hour World Models, & the Bitter Lesson of Robotics

@DynaRobotics’s @JasonMa2020 explains how world-action models predict the future to guide physical robot actions, why training on over a million hours of human video lets robots adapt to new tasks with as little as 45 minutes of demonstrations, how synthetic data and self-improving world models could accelerate robotics, why agentic harnesses are enabling hour-long autonomous workflows, and how the Bitter Lesson of scaling, full-stack hardware, and real-world deployments at Din Tai Fung are turning physical AI from impressive demos into useful robots.

Source: Latent.Space · x.comPublished