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

Find What You Want: Learning Demand-conditioned Object Attribute Space for Demand-driven Navigation