让机器人记住过去合作经验，提升搜救协作效率
Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration
- 用知识图谱存储过往合作模式，自动提取有效经验
- 初始化机器人后救援成功率从25.7%升至41.3%，任务时间减少283秒
- 特别在协作初期效果显著，适合需要快速上手的团队场景
高效的人机协作要求机器人能在交互开始时适应搭档、环境与任务动态。在MATRX城市搜救（USAR）环境中，参与者可通过聊天与反思界面外化其发现的合作模式（CPs）。本文研究机器人是否可利用这些历史经验成为更优队友。为此，我们将过往合作模式表示为知识图谱式的事件记忆，并采用图表示学习结合节点分类目标，识别出可复用的代表性记忆。随后在新协作回合开始前，将该记忆用于初始化机器人。基于20名参与者和160轮观察数据，仅使用一个自动选取的历史合作模式，即可使救援成功率从25.7%提升至41.3%，平均任务时间减少283秒。初期阶段增益最明显，表明可复用的事件记忆能使机器人以更有效的任务知识进入协作，促进早期合作顺畅进行。
原文摘要 · Abstract (English)
Effective human-robot teamwork requires robots to adapt to partners, situations, and task dynamics from the start of an interaction. In the MATRX Urban Search and Rescue (USAR) environment, people can externalize collaboration patterns (CPs) they discover during teamwork through a chat and reflection interface. We study whether a robot can use such prior team experience to become a better teammate in future interactions. To this end, we represent historical CPs as knowledge-graph episodic memories and use graph representation learning with a node-classification objective to identify a representative and effective memory for reuse. We then initialize the robot with this memory before a new collaboration episode begins. Across 20 participants and 160 round-level observations, initializing the robot with a single automatically selected prior CP increases rescue success from 25.7% to 41.3% and reduces average task time by 283 seconds. The strongest gains appear at the beginning of interaction, suggesting that reusable episodic memory can help robots enter collaboration with more effective task knowledge and support smoother early teamwork.
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