  The ability to navigate like a human towards a language-guided target from
anywhere in a 3D embodied environment is one of the 'holy grail' goals of
intelligent robots. Most visual navigation benchmarks, however, focus on
navigating toward a target from a fixed starting point, guided by an elaborate
set of instructions that depicts step-by-step. This approach deviates from
real-world problems in which human-only describes what the object and its
surrounding look like and asks the robot to start navigation from anywhere.
Accordingly, in this paper, we introduce a Scenario Oriented Object Navigation
(SOON) task. In this task, an agent is required to navigate from an arbitrary
position in a 3D embodied environment to localize a target following a scene
description. To give a promising direction to solve this task, we propose a
novel graph-based exploration (GBE) method, which models the navigation state
as a graph and introduces a novel graph-based exploration approach to learn
knowledge from the graph and stabilize training by learning sub-optimal
trajectories. We also propose a new large-scale benchmark named From Anywhere
to Object (FAO) dataset. To avoid target ambiguity, the descriptions in FAO
provide rich semantic scene information includes: object attribute, object
relationship, region description, and nearby region description. Our
experiments reveal that the proposed GBE outperforms various state-of-the-arts
on both FAO and R2R datasets. And the ablation studies on FAO validates the
quality of the dataset.
