用大模型实现人机双向心智模型对齐，提升协作准确性
Bi-Directional Mental Model Reconciliation for Human-Robot Interaction with Large Language Models
- 通过自然语言对话让人类和机器人双向修正各自认知
- 无需预设正确模型，双方可共同发现并补全关键任务信息
- 适合需动态协作的复杂人机交互场景
在人机交互中，人类与机器人会形成对环境、任务及彼此的内部心智模型。这些表征的准确性取决于各主体执行心理理论（Theory of Mind）的能力，即理解对方的知识、偏好与意图。当心智模型偏差过大影响任务执行时，必须进行协调以防止交互质量下降。本文提出一种双向心智模型协调框架，利用大语言模型促进半结构化自然语言对话中的对齐。该框架突破了以往研究中假设人类或机器人一方已有正确对方模型的前提。通过本框架，人与机器人都能在交互过程中识别并沟通缺失的任务相关上下文，逐步达成共享心智模型。
原文摘要 · Abstract (English)
In human-robot interactions, human and robot agents maintain internal mental models of their environment, their shared task, and each other. The accuracy of these representations depends on each agent's ability to perform theory of mind, i.e. to understand the knowledge, preferences, and intentions of their teammate. When mental models diverge to the extent that it affects task execution, reconciliation becomes necessary to prevent the degradation of interaction. We propose a framework for bi-directional mental model reconciliation, leveraging large language models to facilitate alignment through semi-structured natural language dialogue. Our framework relaxes the assumption of prior model reconciliation work that either the human or robot agent begins with a correct model for the other agent to align to. Through our framework, both humans and robots are able to identify and communicate missing task-relevant context during interaction, iteratively progressing toward a shared mental model.
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