  Navigating in unseen environments is crucial for mobile robots. Enhancing
them with the ability to follow instructions in natural language will further
improve navigation efficiency in unseen cases. However, state-of-the-art (SOTA)
vision-and-language navigation (VLN) methods are mainly evaluated in
simulation, neglecting the complex and noisy real world. Directly transferring
SOTA navigation policies trained in simulation to the real world is challenging
due to the visual domain gap and the absence of prior knowledge about unseen
environments. In this work, we propose a novel navigation framework to address
the VLN task in the real world. Utilizing the powerful foundation models, the
proposed framework includes four key components: (1) an LLMs-based instruction
parser that converts the language instruction into a sequence of pre-defined
macro-action descriptions, (2) an online visual-language mapper that builds a
real-time visual-language map to maintain a spatial and semantic understanding
of the unseen environment, (3) a language indexing-based localizer that grounds
each macro-action description into a waypoint location on the map, and (4) a
DD-PPO-based local controller that predicts the action. We evaluate the
proposed pipeline on an Interbotix LoCoBot WX250 in an unseen lab environment.
Without any fine-tuning, our pipeline significantly outperforms the SOTA VLN
baseline in the real world.
