Dynamic Path Navigation for Motion Agents with LLM Reasoning

• URL: http://arxiv.org/abs/2503.07323v1
• Date: Mon, 10 Mar 2025 13:39:09 GMT
• Title: Dynamic Path Navigation for Motion Agents with LLM Reasoning
• Authors: Yubo Zhao, Qi Wu, Yifan Wang, Yu-Wing Tai, Chi-Keung Tang,
• Abstract summary: Large Language Models (LLMs) have demonstrated strong generalizable reasoning and planning capabilities.<n>We explore the zero-shot navigation and path generation capabilities of LLMs by constructing a dataset and proposing an evaluation protocol.<n>We demonstrate that, when tasks are well-structured in this manner, modern LLMs exhibit substantial planning proficiency in avoiding obstacles while autonomously refining navigation with the generated motion to reach the target.
• Score: 69.5875073447454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong generalizable reasoning and planning capabilities. However, their efficacies in spatial path planning and obstacle-free trajectory generation remain underexplored. Leveraging LLMs for navigation holds significant potential, given LLMs' ability to handle unseen scenarios, support user-agent interactions, and provide global control across complex systems, making them well-suited for agentic planning and humanoid motion generation. As one of the first studies in this domain, we explore the zero-shot navigation and path generation capabilities of LLMs by constructing a dataset and proposing an evaluation protocol. Specifically, we represent paths using anchor points connected by straight lines, enabling movement in various directions. This approach offers greater flexibility and practicality compared to previous methods while remaining simple and intuitive for LLMs. We demonstrate that, when tasks are well-structured in this manner, modern LLMs exhibit substantial planning proficiency in avoiding obstacles while autonomously refining navigation with the generated motion to reach the target. Further, this spatial reasoning ability of a single LLM motion agent interacting in a static environment can be seamlessly generalized in multi-motion agents coordination in dynamic environments. Unlike traditional approaches that rely on single-step planning or local policies, our training-free LLM-based method enables global, dynamic, closed-loop planning, and autonomously resolving collision issues.

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