Robots that learn from each other could transform disaster rescue, review finds

Automatically extracted; no manual editorial approval recorded.

ne-to-machine (M2M), and machine-to-human (M2H) learning, and argues that treating them as interdependent layers of a single collaborative pipeline reveals architectural gaps that single-paradigm analyses tend to miss. The stakes are considerable. The review notes that since 1990, disasters have affected more than 4.4 billion people, caused roughly 1.3 million deaths, and produced losses exceeding $2.97 trillion. Rob

Open source S3

Robots that learn from each other could transform disaster rescue, review finds

Open source S1

otic assistance already spans ground robots for debris removal and logistics, aerial drones for damage assessment, survivor identification and hazard mapping, and aquatic robots for water rescues. Advances in machine learning, deep learning and large language models (LLMs) now support autonomous perception, navigation and coordination, reducing human exposure to danger while improving situational awareness. Yet the a

Open source S4

are validated primarily in simulation or custom experimental environments, and the gap between LLM performance in controlled settings and reliable operation in real disaster environments remains substantial. No current LLM-enabled SAR system, they write, has been validated at operational scale against burning structures with intermittent connectivity, degraded sensors, minute-scale time pressure and cognitively overl

Open source S21