LLM A: Human in the Loop Large Language Models Enabled A Search for Robotics¶
Arxiv Link - 2023-12-04 10:37:58
Abstract¶
This research focuses on how Large Language Models (LLMs) can help with path planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner. A novel framework named LLM A, aims to leverage the commonsense of LLMs, and the utility-optimal A is proposed to facilitate few-shot near-optimal path planning. Prompts are used to 1) provide LLMs with essential information like environment, cost, heuristics, etc.; 2) communicate human feedback to LLMs on intermediate planning results. This makes the whole path planning process a `white box' and human feedback guides LLM A to converge quickly compared to other data-driven methods such as reinforcement learning-based (RL) path planning. In addition, it makes code-free path planning practical, henceforth promoting the inclusiveness of artificial intelligence techniques. Comparative analysis against A and RL shows that LLM A is more efficient in terms of search space and achieves an on-a-par path with A and a better path than RL. The interactive nature of LLM A* also makes it a promising tool for deployment in collaborative human-robot tasks.
Socials¶
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| 🚀 Exciting News in AI Research! 🤖 Delving into the realm of Large Language Models (LLMs), a groundbreaking study unveils the potential of LLMs in enhancing path planning for mobile robots. The introduction of LLM A showcases a novel framework that merges LLMs' commonsense with the efficiency of A to revolutionize few-shot near-optimal path planning. By integrating human feedback through prompts, LLM A* emerges as a 'white box' solution, outperforming RL-based methods and streamlining the convergence process. Discover more about this cutting-edge research and the promising implications for collaborative human-robot tasks: Read the full study here! #AI #LLM #PathPlanning #Robotics #Research #Innovation #TechNews |
Exciting research on leveraging Large Language Models (LLMs) for path planning in robots! The novel LLM A framework combines LLM commonsense and A for efficient few-shot path planning. Human feedback guides LLM A* to quick convergence, outperforming RL methods. Learn more at: http://arxiv.org/abs/2312.01797v1 #AI #LLMs #Robotics |