Images are a convenient way to specify which particular object instance an
embodied agent should navigate to. Solving this task requires semantic visual
reasoning and exploration of unknown environments. We present a system that can
perform this task in both simulation and the real world. Our modular method
solves sub-tasks of exploration, goal instance re-identification, goal
localization, and local navigation. We re-identify the goal instance in
egocentric vision using feature-matching and localize the goal instance by
projecting matched features to a map. Each sub-task is solved using
off-the-shelf components requiring zero fine-tuning. On the HM3D
InstanceImageNav benchmark, this system outperforms a baseline end-to-end RL
policy 7x and a state-of-the-art ImageNav model 2.3x (56% vs 25% success). We
deploy this system to a mobile robot platform and demonstrate effective
real-world performance, achieving an 88% success rate across a home and an
office environment.

本研究介绍了一个可以在仿真和现实实现物体到达任务的系统，该系统采用模块化方法解决了探索、目标识别、目标定位和本地导航等子任务，无需任何微调，实现了较高的成功率，并在移动机器人平台上展示了有效的实际性能。