Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration
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...
| Authors | Taewoon Kim, Emma van Zoelen, Mark Neerincx |
|---|---|
| Primary category | cs.HC |
| Published | 17 June 2026 |
| Last updated at source | 28 July 2026 |
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