Abstract
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
Abstract (translated)
我们引入了一种独特的环境,在该环境中,一个代理需要从自然语言中传达相关任务的演示中学习任务。该提议的环境可以提供一个低 effort 的语言描述,以便从其他任务中重用演示,并且可以用于提供反馈以纠正代理错误,这些都是构建协助人类在日常生活中完成任务的 intelligent 代理的重要目标。为了在所提议的环境中实现进展,我们创造了两个基准任务——房间重组和房间导航,涵盖了多种任务适应任务。此外,我们提出了一个框架,使用基于Transformer 模型的模型来考虑任务中的实体及其关系,以学习目标任务的 policy。
URL
https://arxiv.org/abs/2301.09770