AuDeRe is a groundbreaking framework enabling robots to automatically select and implement the most appropriate planning and control strategies for different tasks using LLMs.

• Moves beyond fixed tool integration to enable dynamic strategy selection
• Leverages LLMs to analyze tasks, environments, and robot capabilities
• Demonstrates superior flexibility compared to direct waypoint prediction approaches
• Enhances robotics engineering by allowing context-aware planning and control

This research significantly advances engineering automation by creating more adaptable robotic systems capable of responding to diverse scenarios with optimal control strategies - a critical capability for modern industrial and autonomous applications.

AuDeRe: Automated Strategy Decision and Realization in Robot Planning and Control via LLMs