We introduce a novel setting, wherein an agent needs to learn a task from a demonstration of a related task with the difference between the tasks communicated in natural language. The proposed setting allows reusing demonstrations from other tasks, by providing low effort language descriptions, and can also be used to provide feedback to correct agent errors, which are both important desiderata for building intelligent agents that assist humans in daily tasks. To enable progress in this proposed setting, we create two benchmarks -- Room Rearrangement and Room Navigation -- that cover a diverse set of task adaptations. Further, we propose a framework that uses a transformer-based model to reason about the entities in the tasks and their relationships, to learn a policy for the target task

翻译：我们引入了一种新的环境,使代理人需要从一项相关任务的示范中学习一项任务,这种任务与用自然语言传达的任务之间的差异不同。提议的设置允许通过低努力语言描述,从其他任务中重新使用演示,提供低努力语言描述,并可用于提供反馈,以纠正代理错误。 代理错误对于建设有助于人类日常任务的智能代理非常重要,为了能够在这一拟议设置中取得进展,我们创建了两个基准 -- -- 房间重新安排和房间导航 -- -- 包括一系列不同的任务调整。此外,我们提议了一个框架,利用以变压器为基础的模型来解释这些实体的任务及其关系,以学习目标任务的政策。