arXiv cs.AI· Chalindu Abeywansa, Sahan Gunasekara, Devindi De Silva, Seniru Dissanayake, Ranga Rodrigo, Peshala Jayasekara·· 10 天前AI 评分23
基于阿克曼转向移动机器人的连续环境视觉语言导航 Sim-to-Real 迁移
Sim-to-Real Transfer of Vision-Language Navigation in Continuous Environments Using an Ackermann-Steered Mobile Robot
AI 导读
研究团队将连续环境下的视觉语言导航（VLN）方法从仿真迁移到真实世界，无需导航图或全景视图。系统采用 Cross-Modal Attention（CMA）架构，在仿真数据集上训练后，用自建阿克曼转向机器人（配备摄像头和 LiDAR）采集的真实数据进行微调，结合线性光度调整和少量 episode 微调即可适应真实环境，并在专用硬件上离线运行。SPL 和 nDTW 指标验证了方法的鲁棒性与适应性。
正文 · 原文
Abstract:Vision-Language Navigation (VLN) enables robots to navigate through environments using natural language instructions, making human-robot interaction intuitive. Traditional VLN models often rely on navigation graphs, 360-degree views, and perfect localization which pose significant challenges when adapting these models to real-world settings. This work addresses these limitations by performing a simulation-to-real domain shift of a VLN approach that operates in continuous environments without requiring navigation graphs or panoramic views. The proposed system integrates vision-language models that align visual inputs and linguistic instructions within a shared embedding space, facilitating natural language-driven navigation. We employ a Cross-Modal Attention (CMA) based architecture trained on an existing dataset in a simulated environment and fine-tune it using real-world data collected from a custom-built Ackermann-steered robot equipped with a camera and a LiDAR sensor. By utilising linear photometric adjustments and fine-tuning on a limited number of episodes, our model successfully adapts to real-world environments, achieving effective navigation while running offline on dedicated hardware. Experimental results, evaluated using Success weighted by Path Length (SPL) and Normalized Dynamic Time Warping (nDTW) metrics, demonstrate the robustness and adaptability of our approach. Keywords: Vision-Language Navigation, Cross-Modal Attention, Natural Language Instructions, Sim-to-Real Transfer, Autonomous Navigation, Ackermann-steering.
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2610.07192 [cs.AI] |
| (or arXiv:2610.07192v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07192 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1109/ICCAR69571.2026.11549553
DOI(s) linking to related resources |
Submission history
From: Chalindu Abeywansa Nisal [view email]
[v1]
Mon, 5 Oct 2026 18:09:22 UTC (2,321 KB)
来源：arXiv cs.AI · arxiv.org