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Add model-specific Description summary

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Adds a specialized Description paragraph + bullets (analogous to Cosmos3-Super-Image2Video-4Step) summarizing what this model is and how it differs, while keeping the existing generic Cosmos3 overview.

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  1. README.md +9 -0
README.md CHANGED
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  ## Description
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  Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning.
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  This model is ready for commercial and non-commercial use.
 
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  ## Description
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+ Cosmos3-Edge-Policy-DROID is a robot-manipulation policy in the compact 4B Cosmos3-Edge family, specialized for the DROID robot platform. Given language instructions and visual observations from a DROID robot, it generates robot action trajectories for manipulation and control tasks.
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+ - **What's different from the base model:** Where Cosmos3-Edge is a general-purpose Omnimodal world model, this variant is specialized into a control policy for the DROID platform — optimized to map language instructions plus visual observations directly to executable robot action trajectories for manipulation and control.
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+ - **Input / Output:** Language instruction + DROID visual observations in → robot action trajectories out.
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+ - **Example usage and output:** See the [Usage](https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID#usage) / Quickstart section.
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+ - **Hardware:** The compact 4B size is intended for efficient single-GPU deployment; see [Usage](https://huggingface.co/nvidia/Cosmos3-Edge-Policy-DROID#usage).
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+ Cosmos3-Edge-Policy-DROID was developed by NVIDIA as a part of Cosmos3.
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  Cosmos3 is a collection of Omnimodal world models capable of generating dynamic, high-quality video, image, audio, and action commands from combinations of text, image, video, and action trajectory inputs. It serves as a foundational building block for a broad range of Physical AI applications and research spanning world understanding, world generation, simulation, and embodied policy learning.
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  This model is ready for commercial and non-commercial use.