  The recent rapid development of Large Vision-Language Models (LVLMs) has
indicated their potential for embodied tasks.However, the critical skill of
spatial understanding in embodied environments has not been thoroughly
evaluated, leaving the gap between current LVLMs and qualified embodied
intelligence unknown. Therefore, we construct EmbSpatial-Bench, a benchmark for
evaluating embodied spatial understanding of LVLMs.The benchmark is
automatically derived from embodied scenes and covers 6 spatial relationships
from an egocentric perspective.Experiments expose the insufficient capacity of
current LVLMs (even GPT-4V). We further present EmbSpatial-SFT, an
instruction-tuning dataset designed to improve LVLMs' embodied spatial
understanding.
