STARS: extending interactive task learning with large language models

Abstract

Interactive Task Learning (ITL) enables cognitive agents to learn novel tasks (in one shot) from natural-language instruction and allows humans to customize agents to align with individual preferences. ITL relies on reasoning over and learning from multiple sources of knowledge, a strength of cognitive architectures. However, ITL requires frequent human input, which can be tedious and time-consuming. We evaluate large language models (LLMs) as an additional source of knowledge for ITL. We summarize initial experiments exploring the potential use of LLMs in ITL and then describe a novel method (STARS) that markedly improves the reliability of task learning from LLMs for embodied ITL agents. We demonstrate that LLMs speed agent learning and greatly reduce the human effort required to achieve robust, reliable, and repeatable task performance.