arXiv：cs.AI· Chalindu Abeywansa, Sahan Gunasekara, Devindi De Silva, Seniru Dissanayake, Ranga Rodrigo, Peshala Jayasekara·· 4 小时前AI 评分32
基于 Ackermann 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
研究将连续环境下的视觉语言导航（VLN）模型从仿真迁移到真实世界，无需导航图或全景视图。系统采用跨模态注意力（CMA）架构，在仿真数据集上训练后用自建 Ackermann 转向机器人（搭载摄像头和 LiDAR）采集的真实数据微调，通过线性光度调整和少量 episode 微调即完成适配，并可在专用硬件上离线运行。SPL 与 nDTW 指标验证了方法的鲁棒性与适应性。
正文
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