Language is compositional; an instruction can express multiple relation constraints to hold among objects in a scene that a robot is tasked to rearrange. Our focus in this work is an instructable scene rearranging framework that generalizes to longer instructions and to spatial concept compositions never seen at training time. We propose to represent language-instructed spatial concepts with energy functions over relative object arrangements. A language parser maps instructions to corresponding energy functions and an open-vocabulary visual-language model grounds their arguments to relevant objects in the scene. We generate goal scene configurations by gradient descent on the sum of energy functions, one per language predicate in the instruction. Local vision-based policies then relocate objects to the inferred goal locations. We test our model on established instruction-guided manipulation benchmarks, as well as benchmarks of compositional instructions we introduce. We show our model can execute highly compositional instructions zero-shot in simulation and in the real world. It outperforms language-to-action reactive policies and Large Language Model planners by a large margin, especially for long instructions that involve compositions of multiple spatial concepts.

通过使用语言解析器将语言指示映射到相应的能量函数，并且使用开放式词汇的视觉语言模型将它们的参数接地到场景中的相关对象，我们提出了一种表示语言指导的空间概念的方法，可以通用于更长的指令和在训练时从未见过的空间概念构成。我们在已建立的指令引导操作基准测试以及我们引介的构成指令基准测试上测试我们的模型，并展示了它在模拟和现实世界中都能够执行高度构成的指令，而且其性能明显优于语言到动作反应策略和大型语言模型计划者，特别是针对涉及多个空间概念构成的长指令。

基于能量的模型作为零样本规划器用于组合式场景重排