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    EXIMO teaches a robotics model to imitate its control setup, a post says

    A post describes EXIMO as a Google DeepMind student research project that wraps Gemini around Gemini Robotics to provide human-interpretable, hierarchical control.

    MW
    1 Source, 23d ago, first seen 23d ago

    TLDR

    A post describes EXIMO as using Gemini to guide Gemini Robotics through an external control setup that supports fast iteration. It says imitation training transfers that orchestrated behavior into the robotics model itself. The author argues that absorbing this scaffolding lets the model undergo further reinforcement learning—training through rewards—and potentially go beyond what the hand-designed setup could achieve, setting up another cycle of external control design.

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    707

    1 Source, first seen 23d ago

    Combined views

    707

    1 Source, first seen 23d ago

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    1 comments
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    1 Source

    @m_wulfmeierThis nicely captures the tension of creating and "eating" harnesses throughout physical AI model generations. Great to see EXIMO out - Bhavy's student researcher project at @GoogleDeepMind. Harnesses enable fast iteration. In EXIMO, it's largely Gemini wrapped around Gemini Robotics - VLM wrapping VLA. It's human interpretable and enables effective hierarchical control. But then the model eats the harness. Bhavy setup a simple imitation setup of the orchestrated behavior into the VLA itself. Once the scaffolding is inside the weights, we can again optimize the whole model via RL and extend past what the scaffolding could hand-design --> just to enable the next generation of harness design.

    1 Source

    @m_wulfmeierThis nicely captures the tension of creating and "eating" harnesses throughout physical AI model generations. Great to see EXIMO out - Bhavy's student researcher project at @GoogleDeepMind. Harnesses enable fast iteration. In EXIMO, it's largely Gemini wrapped around Gemini Robotics - VLM wrapping VLA. It's human interpretable and enables effective hierarchical control. But then the model eats the harness. Bhavy setup a simple imitation setup of the orchestrated behavior into the VLA itself. Once the scaffolding is inside the weights, we can again optimize the whole model via RL and extend past what the scaffolding could hand-design --> just to enable the next generation of harness design.