Enabling robots to navigate following diverse language instructions in
unexplored environments is an attractive goal for human-robot interaction.
However, this goal is challenging because different navigation tasks require
different strategies. The scarcity of instruction navigation data hinders
training an instruction navigation model with varied strategies. Therefore,
previous methods are all constrained to one specific type of navigation
instruction. In this work, we propose InstructNav, a generic instruction
navigation system. InstructNav makes the first endeavor to handle various
instruction navigation tasks without any navigation training or pre-built maps.
To reach this goal, we introduce Dynamic Chain-of-Navigation (DCoN) to unify
the planning process for different types of navigation instructions.
Furthermore, we propose Multi-sourced Value Maps to model key elements in
instruction navigation so that linguistic DCoN planning can be converted into
robot actionable trajectories. With InstructNav, we complete the R2R-CE task in
a zero-shot way for the first time and outperform many task-training methods.
Besides, InstructNav also surpasses the previous SOTA method by 10.48% on the
zero-shot Habitat ObjNav and by 86.34% on demand-driven navigation DDN. Real
robot experiments on diverse indoor scenes further demonstrate our method's
robustness in coping with the environment and instruction variations.

通过引入动态导航链和价值地图，以及零样本学习的方式，提出了 InstructNav 系统来解决多种导航指令的统一规划，该系统在任务完成上优于以往方法。