aiqbench analysis
This research addresses long-term navigation challenges for service robots by integrating visual perception with Large Language Models to eliminate reliance on drift-prone global maps. The proposed framework generates sequential action plans based on local cues and user commands, enabling safe operation in both known and unknown environments without traditional mapping overhead. Experiments demonstrate significant improvements over standard path planning algorithms like A* and RRT*, offering a robust solution for autonomous service robotics.
Multi-Task Visual Perception Network with LLM Conditioning for Autonomous Navigation
arXiv:2609.14297v1 Announce Type: new 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 https://github.com/PraveenSingh24/VL-Navigation.
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.
Accepted and presented at the IEEE World Congress on Computational Intelligence (IEEE WCCI / IJCNN 2026), Maastricht, Netherlands. \c{opyright} 2026 IEEE. Personal use of this material is permitted
Robotics (cs.RO)
arXiv:2609.14297 [cs.RO]
(or arXiv:2609.14297v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.14297 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 213 words
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