Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models
- URL: http://arxiv.org/abs/2407.02220v2
- Date: Thu, 4 Jul 2024 01:42:58 GMT
- Title: Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models
- Authors: Xiangrui Kong, Wenxiao Zhang, Jin Hong, Thomas Braunl,
- Abstract summary: We propose an LLM-embodied path planning framework for mobile agents.
Our proposed multi-layer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators.
Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks.
- Score: 6.860460230412773
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and solving mathematical problems, leading to advancements in various fields. We propose an LLM-embodied path planning framework for mobile agents, focusing on solving high-level coverage path planning issues and low-level control. Our proposed multi-layer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators. To evaluate the performance of various LLMs, we propose a coverage-weighted path planning metric to assess the performance of the embodied models. Our experiments show that the proposed framework improves LLMs' spatial inference abilities. We demonstrate that the proposed multi-layer framework significantly enhances the efficiency and accuracy of these tasks by leveraging the natural language understanding and generative capabilities of LLMs. Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks. We also tested three LLM kernels: gpt-4o, gemini-1.5-flash, and claude-3.5-sonnet. The experimental results show that claude-3.5 can complete the coverage planning task in different scenarios, and its indicators are better than those of the other models.
Related papers
- Affordances-Oriented Planning using Foundation Models for Continuous Vision-Language Navigation [62.76017573929462]
LLM-based agents have demonstrated impressive zero-shot performance in the vision-language navigation (VLN) task.
We propose AO-Planner, a novel affordances-oriented planning framework for continuous VLN task.
Our method establishes an effective connection between LLM and 3D world to circumvent the difficulty of directly predicting world coordinates.
arXiv Detail & Related papers (2024-07-08T12:52:46Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
We construct a benchmark suite encompassing both classical planning domains and natural language scenarios.
Second, we investigate the use of in-context learning (ICL) to enhance LLM planning, exploring the direct relationship between increased context length and improved planning performance.
Third, we demonstrate the positive impact of fine-tuning LLMs on optimal planning paths, as well as the effectiveness of incorporating model-driven search procedures.
arXiv Detail & Related papers (2024-06-18T22:57:06Z) - From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world.
Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting.
We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
arXiv Detail & Related papers (2024-05-30T09:42:54Z) - LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning [78.2390460278551]
Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation.
Here, we present LLM3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface.
Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning.
arXiv Detail & Related papers (2024-03-18T08:03:47Z) - Understanding the planning of LLM agents: A survey [98.82513390811148]
This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability.
Comprehensive analyses are conducted for each direction, and further challenges in the field of research are discussed.
arXiv Detail & Related papers (2024-02-05T04:25:24Z) - A Prefrontal Cortex-inspired Architecture for Planning in Large Language
Models [16.475564538598768]
Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning.
We take inspiration from the human brain, in which planning is accomplished via the recurrent interaction of specialized modules in the prefrontal cortex (PFC)
arXiv Detail & Related papers (2023-09-30T00:10:14Z) - On the Planning Abilities of Large Language Models : A Critical
Investigation [34.262740442260515]
We evaluate the effectiveness of LLMs in generating plans autonomously in commonsense planning tasks.
In the LLM-Modulo setting, we demonstrate that LLM-generated plans can improve the search process for underlying sound planners.
arXiv Detail & Related papers (2023-05-25T06:32:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.