3P-LLM: Probabilistic Path Planning using Large Language Model for Autonomous Robot Navigation

The paper 3P-LLM presents a novel application of LLMs in the context of autonomous robot navigation. By leveraging the semantic knowledge encoded in language models such as OpenAI’s GPT-3.5-turbo, robots can execute high-level, complex commands described in natural language. Overcoming the challenge posed by language models’ lack of real-world experience, this research demonstrates the feasibility of LLM-assisted path planning for robots.

• Assess the applicability of GPT-3.5-turbo for robotic path planning amid complex environments and unpredictable conditions.
• Leverages the natural language processing prowess of LLMs to offer effective, adaptive path-planning algorithms in real-time.
• In simulated scenarios, GPT-3.5-turbo performed notably well, surpassing Rapidly Exploring Random Tree (RRT) and A* algorithms in path planning tasks.
• Establishes the groundwork for integrating LLMs into robotic systems for enhanced path planning outcomes.

3P-LLM is a groundbreaking exploration into using language models to inform robotics, potentially transforming how robots interpret natural language instructions and navigate through their environments. The successful implementation of LLM in such a practical application signifies the versatility of these models beyond conversational tasks and into tangible physical world interactions. The complete study is shared on arXiv.