让机器人通过记忆积累经验，实现智能辅导与社交互动
Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning
- 用大模型+记忆系统构建可自适应的机器人导师
- 能自主推进训练任务并记住过往互动细节
- 适合需要个性化陪伴的教育或康复场景
将机器人融入日常辅导或体能训练等场景，需其具备自适应、社交性及目标导向的交互能力。尽管大语言模型在类人沟通方面表现优异，但独立使用时受限于记忆能力与上下文连贯性。本文提出一种多模态、受认知启发的框架，增强基于大模型的自主决策能力，在社交与任务导向的人机交互中实现更优表现。具体构建了一个面向机器人教练的智能体，平衡社交对话与任务引导、目标驱动激励。为提升自主性与个性化，引入记忆系统以选择、存储和检索经验，支持跨交互积累的知识推理。初步人机交互用户研究与基于合成数据集的离线实验验证了该方法，表明系统能够管理复杂交互，自主推动训练任务，并建立与调用上下文记忆，推动社交智能机器人的发展。
原文摘要 · Abstract (English)
Integrating robotics into everyday scenarios like tutoring or physical training requires robots capable of adaptive, socially engaging, and goal-oriented interactions. While Large Language Models show promise in human-like communication, their standalone use is hindered by memory constraints and contextual incoherence. This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction. Specifically, we develop an LLM-based agent for a robot trainer, balancing social conversation with task guidance and goal-driven motivation. To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences, facilitating generalized reasoning based on knowledge built across different interactions. A preliminary HRI user study and offline experiments with a synthetic dataset validate our approach, demonstrating the system's ability to manage complex interactions, autonomously drive training tasks, and build and retrieve contextual memories, advancing socially intelligent robotics.
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