用空间关系增强3D场景图，提升机器人理解自然语言指令的能力

Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural language command requires the robot to infer the intended task and how to decompose it into executable actions, and to ground those actions in the robot's knowledge of the environment, including relevant objects, agents, and locations. This challenge can be addressed by combining the capabilities of large language models (LLMs) to understand natural language with 3D scene graphs (3DSGs) for grounding inferred actions in a semantic representation of the environment. However, many 3DSGs lack explicit spatial relations between objects, even though humans often rely on these relations to describe an environment. This paper investigates whether incorporating open- or closed-vocabulary spatial relations into 3DSGs can improve the ability of LLMs to interpret natural language commands. To address this, we implement two pipelines using off-the-shelf models: an LLM-based pipeline for target object grounding from open-vocabulary language commands and a vision language model-based pipeline to add open-vocabulary spatial edges to 3DSGs from images captured while mapping. Finally, we evaluate two LLMs across 14 scenes using 905 natural language statements (786 procedurally-generated, 119 human-authored) to assess performance on the downstream task of target object grounding. Our study demonstrates that explicit spatial relations improve the ability of LLMs to ground objects, and while open-vocabulary relation generation with vision language models proves feasible from robot-captured images, our analysis did not yield evidence favoring either open- or closed-vocabulary relations.