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World-action models and human video for robot training

Latent Space says Dyna Robotics’ Jason Ma explains how world-action models predict the future to guide robots’ physical actions.

swyx 🗽 @aiDotEngineerS🗽
2 Sources, 9h ago, first seen 9h ago

TLDR

Latent Space says Dyna Robotics’ Jason Ma argues that training on over a million hours of human video lets robots adapt to new tasks with as little as 45 minutes of demonstrations. He also discusses how synthetic data and self-improving world models could accelerate robotics, alongside hour-long autonomous workflows and real-world deployments at Din Tai Fung.

Combined views

315

2 Sources, first seen 9h ago

11 reposts

Combined views

315

2 Sources, first seen 9h ago

11 reposts
Robots That Predict the Future: Dyna-2 & Physical AI — Jason Ma, Dyna Robotics
Image: Latent Space on YouTube
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2 Sources

Latent.Space@latentspacepodRobots 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.9h
swyx 🗽 @aiDotEngineer@swyxRT @latentspacepod: Robots That Predict the Future: Dyna-2, Million-Hour World Models, & the Bitter Lesson of Robotics @DynaRobotics’s @Ja…2h
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    2 Sources

    Latent.Space@latentspacepodRobots 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.9h
    swyx 🗽 @aiDotEngineer@swyxRT @latentspacepod: Robots That Predict the Future: Dyna-2, Million-Hour World Models, & the Bitter Lesson of Robotics @DynaRobotics’s @Ja…2h
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