VLFM: Vision-Language Frontier Maps for Semantic Navigation
ICRA• 2024
Abstract
Understanding how humans leverage semantic knowledge to navigate unfamiliar
environments and decide where to explore next is pivotal for developing robots
capable of human-like search behaviors. We introduce a zero-shot navigation
approach, Vision-Language Frontier Maps (VLFM), which is inspired by human
reasoning and designed to navigate towards unseen semantic objects in novel
environments. VLFM builds occupancy maps from depth observations to identify
frontiers, and leverages RGB observations and a pre-trained vision-language
model to generate a language-grounded value map. VLFM then uses this map to
identify the most promising frontier to explore for finding an instance of a
given target object category. We evaluate VLFM in photo-realistic environments
from the Gibson, Habitat-Matterport 3D (HM3D), and Matterport 3D (MP3D)
datasets within the Habitat simulator. Remarkably, VLFM achieves
state-of-the-art results on all three datasets as measured by success weighted
by path length (SPL) for the Object Goal Navigation task. Furthermore, we show
that VLFM's zero-shot nature enables it to be readily deployed on real-world
robots such as the Boston Dynamics Spot mobile manipulation platform. We deploy
VLFM on Spot and demonstrate its capability to efficiently navigate to target
objects within an office building in the real world, without any prior
knowledge of the environment. The accomplishments of VLFM underscore the
promising potential of vision-language models in advancing the field of
semantic navigation. Videos of real-world deployment can be viewed at
naoki.io/vlfm.