To perform tasks specified by natural language instructions, autonomous
agents need to extract semantically meaningful representations of language and
map it to visual elements and actions in the environment. This problem is
called task-oriented language grounding. We propose an end-to-end trainable
neural architecture for task-oriented language grounding in 3D environments
which assumes no prior linguistic or perceptual knowledge and requires only raw
pixels from the environment and the natural language instruction as input. The
proposed model combines the image and text representations using a
Gated-Attention mechanism and learns a policy to execute the natural language
instruction using standard reinforcement and imitation learning methods. We
show the effectiveness of the proposed model on unseen instructions as well as
unseen maps, both quantitatively and qualitatively. We also introduce a novel
environment based on a 3D game engine to simulate the challenges of
task-oriented language grounding over a rich set of instructions and
environment states.

提出一种基于端到端可训练神经网络架构，用于在 3D 环境中执行自然语言指令的任务导向语言接地问题，并使用带门控的注意力机制来组合图像和文本表示，并学习执行任务的策略。在一个新的基于 3D 游戏引擎的环境中展示了该模型在未见过的指令和环境下的有效性。