Natural language serves as the primary mode of communication when an
intelligent agent with a physical presence engages with human beings. While a
plethora of research focuses on natural language understanding (NLU),
encompassing endeavors such as sentiment analysis, intent prediction, question
answering, and summarization, the scope of NLU directed at situations
necessitating tangible actions by an embodied agent remains limited. The
inherent ambiguity and incompleteness inherent in natural language present
challenges for intelligent agents striving to decipher human intention. To
tackle this predicament head-on, we introduce a novel system known as task and
argument grounding for Embodied agents (tagE). At its core, our system employs
an inventive neural network model designed to extract a series of tasks from
complex task instructions expressed in natural language. Our proposed model
adopts an encoder-decoder framework enriched with nested decoding to
effectively extract tasks and their corresponding arguments from these
intricate instructions. These extracted tasks are then mapped (or grounded) to
the robot's established collection of skills, while the arguments find
grounding in objects present within the environment. To facilitate the training
and evaluation of our system, we have curated a dataset featuring complex
instructions. The results of our experiments underscore the prowess of our
approach, as it outperforms robust baseline models.

提出了一种名为 tagE 的新系统，采用神经网络模型来从复杂的自然语言任务说明中提取一系列任务和相应的参数，并将这些任务映射到机器人的技能集合中，参数与环境中的对象关联。实验证明这种方法胜过坚实的基线模型。