Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the given task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robot-simulation results demonstrate that a system successfully interpreting both goal-oriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.

本研究提出了深度递归动作-目标基础网络（DRAGGN）用于任务的基础和执行，其处理输入的任何类别的自然语言，并且通用于未见过的环境，在仿真机器人的实验中取得成功，这使我们更接近于为人机交互实现稳健的自然语言理解。

两个DRAGGN的故事：一种混合方法解释以行动和目标为导向的指令