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