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.

翻译：语言具有组合性；一条指令可以表达多个关系约束，这些约束作用于机器人需要重排的场景中的物体。本文聚焦于构建一个可指令驱动的场景重排框架，该框架能够泛化到更长的指令以及训练阶段从未见过的空间概念组合。我们提出用相对物体排列上的能量函数来表示语言指令驱动的空间概念。语言解析器将指令映射为对应的能量函数，而开放词汇的视觉语言模型则将其参数与场景中的相关物体进行绑定。我们通过对各能量函数（每个对应于指令中的一个语言谓词）之和进行梯度下降，来生成目标场景配置。随后，基于局部视觉的策略将物体重新定位到推断出的目标位置。我们在已有的指令驱动操作基准测试以及我们新引入的组合指令基准测试上对模型进行了评估。结果表明，我们的模型能够在仿真和真实世界中零样本地执行高度组合性的指令。相比语言到动作的反应式策略和大语言模型规划器，我们的模型表现大幅领先，尤其是在涉及多个空间概念组合的较长指令场景中。