This research demonstrates how GPT-4 can drive humanoid robot behavior with a novel approach incorporating real-time state feedback and a reactive behavior framework.

• Implements LLM-driven robot control in both simulation and real-world environments
• Leverages continuous state feedback to help GPT-4 adapt strategies dynamically
• Creates a reactive behavior system that enhances safety and reliability
• Provides a promising approach for more autonomous robotic systems

This engineering advancement matters because it addresses critical safety concerns in AI-powered robotics while enabling more adaptive and responsive robotic behavior—potentially accelerating practical applications in various industries.

Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework