arXiv cs.AI· Taewoon Kim, Emma van Zoelen, Mark Neerincx·· 14 小时前AI 评分27
利用先前协作的情景记忆提升城市搜救中的人机团队协作
Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration
AI 导读
研究将历史协作模式表示为知识图谱情景记忆，通过图表示学习与节点分类目标自动选出可复用的代表性记忆，并在新协作开始前初始化机器人。在 MATRX 城市搜救环境中，20 名参与者、160 次回合级观测显示，该初始化使救援成功率从 25.7% 提升至 41.3%，平均任务时间减少 283 秒，增益在交互初期最明显。
正文 · 原文
Abstract: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.
| Comments: | Accepted at IEEE RO-MAN 2026 |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18836 [cs.HC] |
| (or arXiv:2606.18836v3 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18836 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.1109/RO-MAN72286.2026.11714607
DOI(s) linking to related resources |
Submission history
From: Taewoon Kim [view email]
[v1]
Wed, 17 Jun 2026 09:10:59 UTC (1,264 KB)
[v2]
Tue, 28 Jul 2026 13:23:11 UTC (1,264 KB)
[v3]
Thu, 29 Oct 2026 16:50:38 UTC (1,268 KB)
来源：arXiv cs.AI · arxiv.org