  The task of Visual Object Navigation (VON) involves an agent's ability to
locate a particular object within a given scene. In order to successfully
accomplish the VON task, two essential conditions must be fulfilled:1) the user
must know the name of the desired object; and 2) the user-specified object must
actually be present within the scene. To meet these conditions, a simulator can
incorporate pre-defined object names and positions into the metadata of the
scene. However, in real-world scenarios, it is often challenging to ensure that
these conditions are always met. Human in an unfamiliar environment may not
know which objects are present in the scene, or they may mistakenly specify an
object that is not actually present. Nevertheless, despite these challenges,
human may still have a demand for an object, which could potentially be
fulfilled by other objects present within the scene in an equivalent manner.
Hence, we propose Demand-driven Navigation (DDN), which leverages the user's
demand as the task instruction and prompts the agent to find the object matches
the specified demand. DDN aims to relax the stringent conditions of VON by
focusing on fulfilling the user's demand rather than relying solely on
predefined object categories or names. We propose a method first acquire
textual attribute features of objects by extracting common knowledge from a
large language model. These textual attribute features are subsequently aligned
with visual attribute features using Contrastive Language-Image Pre-training
(CLIP). By incorporating the visual attribute features as prior knowledge, we
enhance the navigation process. Experiments on AI2Thor with the ProcThor
dataset demonstrate the visual attribute features improve the agent's
navigation performance and outperform the baseline methods commonly used in
VON.
