Navigation Instruction

Navigation instruction generation focuses on enabling agents, both robotic and virtual, to understand and produce natural language instructions for navigating environments. Current research heavily utilizes large language models (LLMs), often incorporating bird's-eye-view (BEV) representations of the environment and employing techniques like chain-of-thought prompting and instruction tuning to improve instruction quality and controllability. This field is significant for advancing human-robot interaction and autonomous navigation, with recent work demonstrating improved performance on various benchmarks through the use of synthetic data and innovative training methods.