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Pretrained LLMs can drive complex robot navigation tasks across diverse environments and requests without any navigation-specific fine-tuning, by offloading motion execution to specialized tools.
Photorealistic simulation with Gaussian Splatting and drivable avatars closes the reality gap, enabling embodied agents to learn human-aware navigation policies that generalize better to the real world.
