Language Navigation

Language navigation (VLN) focuses on enabling robots to navigate environments based on natural language instructions and visual input, aiming to bridge the gap between human communication and robotic action. Current research emphasizes improving the robustness and generalization of VLN agents, often employing transformer-based architectures, large language models (LLMs), and novel scene representation methods like volumetric environments and visual language pose graphs to enhance spatial understanding and decision-making. This field is significant for advancing embodied AI and has potential applications in assistive robotics for the visually impaired, autonomous vehicle navigation, and human-robot collaboration in complex environments.