Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration

2026-06-17

AI Summary

This study addresses the challenge of low initial coordination efficiency in urban search and rescue scenarios, where robots struggle to rapidly adapt to dynamic human-robot collaborative environments. To overcome this limitation, the work introduces a reusable episodic memory mechanism that encodes historical collaboration patterns into a knowledge graph. By integrating graph representation learning, the system automatically retrieves and initializes optimal behavioral policies prior to new tasks, enabling effective cross-task knowledge transfer. Experimental evaluations on the MATRX simulation platform demonstrate significant improvements in collaborative performance: rescue success rates increase from 25.7% to 41.3%, and average task completion time is reduced by 283 seconds, with particularly pronounced gains during the early phases of missions.

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.

Problem

• human-robot teamwork
• urban search and rescue
• episodic memory
• collaboration patterns
• task adaptation

Innovation

• episodic memory
• human-robot teamwork
• collaboration patterns
• graph representation learning
• knowledge graph