Models that can execute natural language instructions for situated robotic tasks such as assembly and navigation have several useful applications in homes, offices, and remote scenarios. We study the semantics of spatially-referred configuration and arrangement instructions, based on the challenging blank-labeled block dataset of Bisk et al. (2016). This task involves finding a source block and moving it to the target position (mentioned via a reference block and offset), where the blocks have no names or colors and are just referred to via spatial location features. We present novel models for the subtasks of source block classification and target position regression, based on joint-loss language and spatial-world representation learning, as well as CNN-based and dual attention models to compute the alignment between the world blocks and the instruction phrases. For target position prediction, we compare two inference approaches: annealed sampling via policy gradient versus expectation inference via supervised regression. Our models achieve the new state-of-the-art on this task, with an improvement of 47% on source block accuracy and 22% on target position distance.

研究在家庭、办公室和远程场景中执行自然语言指令的模型，探讨基于 Bisk-2016 数据集语言和空间世界表示学习的源块分类和目标位置回归的新型模型，CNN 和双重注意力模型用于计算世界块和指令短语之间的对齐，深度比较了两种推理方法并取得了最新的业内最佳效果。

面向空间指令理解的源目推理模型