Navigation Model

Navigation models aim to enable autonomous agents to efficiently and reliably reach specified goals within complex environments, using various input modalities like images, language descriptions, and 3D coordinates. Current research emphasizes developing robust, generalizable models capable of handling diverse goal specifications and dynamic environments, often employing modular architectures, graph-based representations, and techniques like reinforcement learning and large language model integration. This work is crucial for advancing robotics, autonomous vehicles, and other applications requiring intelligent spatial reasoning and decision-making, particularly in unstructured or unpredictable settings.