We aim to solve the problem of spatially localizing composite instructions referring to space: space grounding. Compared to current instance grounding, space grounding is challenging due to the ill-posedness of identifying locations referred to by discrete expressions and the compositional ambiguity of referring expressions. Therefore, we propose a novel probabilistic space-grounding methodology (LINGO-Space) that accurately identifies a probabilistic distribution of space being referred to and incrementally updates it, given subsequent referring expressions leveraging configurable polar distributions. Our evaluations show that the estimation using polar distributions enables a robot to ground locations successfully through $20$ table-top manipulation benchmark tests. We also show that updating the distribution helps the grounding method accurately narrow the referring space. We finally demonstrate the robustness of the space grounding with simulated manipulation and real quadruped robot navigation tasks. Code and videos are available at https://lingo-space.github.io.

翻译：本文旨在解决复合指令中空间定位的语言空间锚定问题。相比现有的实例锚定，空间锚定因离散表达式所指位置的病态性及指代表达的组合歧义而更具挑战性。为此，我们提出一种新颖的概率空间锚定方法（LINGO-Space），该方法能准确识别所指空间的概率分布，并利用可配置的极坐标分布，在后续指代表达出现时对其进行增量更新。实验表明，基于极坐标分布的估计方法使机器人能够在20组桌面操作基准测试中成功完成位置锚定。我们还证明，分布更新有助于锚定方法精确缩小所指空间范围。最后，通过仿真操作任务与真实四足机器人导航任务验证了空间锚定的鲁棒性。代码与视频见https://lingo-space.github.io。