The October 22, 2025 GigaAI paper introduces **GigaBrain-0**, a novel Vision-Language-Action (VLA) model designed for general-purpose robotic systems, which is primarily trained using a combination of real-world robot data and synthetic data generated by a world model called **GigaWorld**. This approach aims to enhance generalization across various real-world conditions by leveraging diverse synthetic data streams like **Real2Real Transfer**, **Sim2Real Transfer**, and **View Transfer**. Architecturally, GigaBrain-0 incorporates **RGB-D input modeling** for better spatial reasoning and uses an **embodied Chain-of-Thought (CoT)** framework that generates intermediate reasoning steps such as manipulation trajectories and subgoal language. Experimental results across dexterous manipulation, long-horizon, and mobile manipulation tasks demonstrate that the model, particularly when augmented with world model-generated data, achieves superior performance and robustness compared to baseline models like $\pi0$. The paper also presents GigaBrain-0-Small, an optimized variant for efficient hardware deployment.Source:https://arxiv.org/pdf/2510.19430

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