This research introduces a retrospective framework that significantly improves how robots work together in complex, uncertain environments.

• Integrates LLM capabilities with a novel actor-critic architecture
• Enables robots to learn from past experiences and adapt decisions
• Enhances coordination in dynamic real-world scenarios
• Improves collaborative decision-making efficiency

This advancement matters for engineering by bridging the gap between AI language capabilities and practical robotic collaboration, potentially transforming industries like manufacturing, logistics, and emergency response where robot teams must operate reliably in unpredictable situations.

Integrating Retrospective Framework in Multi-Robot Collaboration