The publication presents a system enabling quadrupedal robots to execute challenging, long-horizon tasks with the aid of Large Language Models (LLMs). The system is composed of multiple LLM agents that engage in high-level reasoning to formulate hybrid plans, converting them into executable robot code. Alongside, a reinforcement learning foundation provides powerful motion planning and control skills for dynamic environmental interactions.

Summary:

• Introduces an LLM-based system to guide quadruped robots in complex tasks.
• Features a semantic planner, parameter calculator, and code generator.
• Adopts reinforcement learning for versatile motion planning and control.
• Demonstrates successful multi-step strategies in simulations and real-world scenarios.
• Highlights the potential of LLMs in enhancing robot problem-solving abilities.

This system represents a major leap in robotic capabilities, merging the abstract thinking prowess of LLMs with practical locomotion and manipulation skills. The quadrupeds’ ability to solve long-horizon tasks and exhibit multi-step strategies is a testament to the synergistic potential of AI and robotics. The research offers a glimpse into a future where robots can autonomously navigate complex environments and aid in tasks ranging from industrial applications to search and rescue missions. Discover the full study on arXiv.