  In Zero-Shot ObjectNav, an embodied ground agent is expected to navigate to a
target object specified by a natural language label without any
environment-specific fine-tuning. This is challenging, given the limited view
of a ground agent and its independent exploratory behavior. To address these
issues, we consider an assistive overhead agent with a bounded global view
alongside the ground agent and present two coordinated navigation schemes for
judicious exploration. We establish the influence of the Generative
Communication (GC) between the embodied agents equipped with Vision-Language
Models (VLMs) in improving zero-shot ObjectNav, achieving a 10% improvement in
the ground agent's ability to find the target object in comparison with an
unassisted setup in simulation. We further analyze the GC for unique traits
quantifying the presence of hallucination and cooperation. In particular, we
identify a unique trait of "preemptive hallucination" specific to our embodied
setting, where the overhead agent assumes that the ground agent has executed an
action in the dialogue when it is yet to move. Finally, we conduct real-world
inferences with GC and showcase qualitative examples where countering
pre-emptive hallucination via prompt finetuning improves real-world ObjectNav
performance.
