大模型
arxiv.org ·
用于自主导航的LLM条件化多任务视觉感知网络
该论文针对服务机器人长期导航中里程计漂移与传感器误差累积导致 2D 地图退化、传统路径规划（A*、RRT*、DiPPer、ViT-A*）失效的问题，提出一种交互式框架：不依赖全局一致地图，而是结合视觉感知与 LLM 解析文本或语音指令，基于局部视觉线索和自中心几何指令生成顺序动作计划并逐步执行，通过相对即时目标重置定位以最小化漂移累积，实现已知与未知环境中的无碰撞导航。作者称在真实与仿真数据上优于其他方法，源码已公开。
AI推荐理由 arXiv 机器人学（cs.RO）新论文，提出用 LLM 解释文本/语音指令、基于局部视觉线索与自中心几何指令生成顺序动作计划，从而摆脱对全局一致地图的依赖，缓解里程计漂移与传感器误差导致的 2D 地图退化问题；作者称在真实与仿真数据上相较其他方法有显著提升，并公开源码。局限在于摘要未给出具体量化指标、对比基线和消融细节，'显著提升'缺乏可验证数据，且未说明真实机器人平台与场景规模。
72
正文
Computer Science > Robotics
arXiv:2609.14297 (cs)
[Submitted on 13 Sep 2026]
Title:Multi-Task Visual Perception Network with LLM Conditioning for Autonomous Navigation
Authors:Praveen Kumar, K. R. Guruprasad, Tushar Sandhan
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Abstract:Long-term navigation for service robots faces crit- ical challenges like the accumulation of odometry drift and sensor error, which progressively degrade 2D maps and renders traditional path planning algorithms (e.g., A*, RRT*, DiPPer, ViT-A*) ineffective over time. To address this, we propose a user-friendly, interactive framework that eliminates the reliance on globally consistent maps. Our approach integrates visual perception with Large Language Models (LLM) to interpret user commands via text or voice. Instead of relying on a drift- prone global map, the system generates a sequential action plan based on local visual cues and egocentric geometric instructions. These action plans are executed sequentially, allowing the robot to navigate known and unknown environments safely. By reset- ting localization relative to immediate targets, our framework effectively works with a minimum accumulation drift strategy, ensuring accurate, efficient, and collision-free navigation without the maintenance overhead of traditional mapping. Experiments on real-world and simulated data have shown significant improve- ments over other methods. Our source code is publicly accessible at this https URL.
Comments:
Subjects:
Robotics (cs.RO)
ACM classes:
I.2.9; I.2.10
Cite as:
arXiv:2609.14297 [cs.RO]
(or
arXiv:2609.14297v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.14297
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Praveen Kumar [view email]
[v1]
Sun, 13 Sep 2026 05:38:52 UTC (2,044 KB)
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