让机器人听懂人话并执行任务，需融合语言模型与交互学习能力。
Challenges in Grounding Language in the Real World
- 用大语言模型+认知代理实现语言与物理世界的对接。
- 提出可交互学习的机器人系统，支持自然语言指令理解。
- 适合研究人机协作、具身智能的开发者参考。
人工智能的长期目标是构建一个语言理解系统，使人类能以自然语言与实体机器人协同工作。本文指出实现该目标面临的关键挑战，并提出一种解决方案：将具备物理世界交互任务学习能力的认知代理与大型语言模型的语言理解能力相结合。同时，本文为该方法的初步实现提供了方向。
原文摘要 · Abstract (English)
A long-term goal of Artificial Intelligence is to build a language understanding system that allows a human to collaborate with a physical robot using language that is natural to the human. In this paper we highlight some of the challenges in doing this, and propose a solution that integrates the abilities of a cognitive agent capable of interactive task learning in a physical robot with the linguistic abilities of a large language model. We also point the way to an initial implementation of this approach.
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