AnchorVLN is an open‑vocabulary vision‑language navigation system that separates semantic proposals from geometric metrics. It uses a VLM to generate semantics while a geometry module supplies reliable metric quantities such as range and bearing, all within a Model Context Protocol server. The system achieves 64.4% on instruction following and improves object‑reference accuracy, reducing median center error from 3.37 m to 2.48 m.
By Long Giang Vu, Chengkai Yao, Yuxin Liu, FNU Aryan, Rajath Chandrashekar Aralikatti
arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.
By Kailing Li, Tianwen Qian, Lijin Yang, Yuqian Fu, Jingyu Gong, Xiaoling Wang, Liang He
arXiv:2608.23354v1 Announce Type: cross
Abstract: Autonomous indoor navigation requires both semantic understanding and precise geometric control. We propose OptiSight, a hybrid framework that combin...
By Alperen Avan, Jordi Sanchez-Riera
arXiv:2607. 08359v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables UAV autonomous navigation in unknown environments by mapping language instructions to real-time visual inputs.
By Xueke Zhu, Qingyan Meng, Liutao Yu, Wei Zhang, Zhengyu Ma, Huihui Zhou, Yonghong Tian
arXiv:2609.06476v1 Announce Type: cross
Abstract: Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to navigate unseen environments by following natural la...
By Shiqi Pan, Qi Zheng, Hanqin Sun, Youjian Zhang, Daquan Feng, Xu Wang
arXiv:2609.08442v1 Announce Type: cross
Abstract: Aerial Vision-and-Language Navigation requires drones to follow natural-language instructions and navigate through complex urban environments. Accura...
By Shanwei Fan, Bin Zhang, Zhiwei Xu, Yingxuan Teng, Siqi Dai, Lin Cheng, Guoliang Fan
arXiv:2606. 30696v1 Announce Type: cross Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging.
By Kaier Liang, Hengde Dai, Cristian-Ioan Vasile
arXiv:2608. 09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egocentric visual observations.
By Zeyuan Ma, Jiaxin Chen, Di Huang
arXiv:2607. 10383v1 Announce Type: cross Abstract: Visual Language Navigation foundation models aim to unify deep reasoning for grounded spatial decisions with broad versatility for diverse embodied tasks.
By Ruiyan Gong, Yingnan Guo, Junjun Hu, Jintao Kong, Xiaoxu Leng, Tianlun Li, Weize Li, Fei Liu, Zhicheng Liu, Jia Lu, Minghua Luo, Chenlin Ming, Yanfen Shen, Jiyue Tao, Zhengbo Wang, Mingyang Yin, Minqi Gu, Zihao Guan, Wei Guo, Guoqing Liu, Huachong Pang, Menglin Yang, Zeqian Ye, Xiaoxiao Geng, Zhining Gu, Honglin Han, Di Jing, Hongyu Pan, Mingchao Sun, Kuan Yang, Jianfang Zhang, Yanghong Chen, Ye He, Wei Mei, Jiahao Shi, Xiangpo Yang, Yanqing Zhu, Zedong Chu, Xiaolong Wu, Mu Xu
arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.
By Jiabin Lou, Haopeng Wang, Yuanshuai Wang, Xinyu Liu, Xuxin Lv, Yuxin Guo, Lei Huang, Rongye Shi, Wenjun Wu
SatNav is a new, scalable benchmark for long‑horizon vision‑language navigation (VLN) with unmanned aerial vehicles (UAVs), built from high‑resolution satellite imagery. It generates 118,000 navigation episodes across 59 scenes in 18 cities, using satellite crops to approximate UAV nadir views and featuring three task families—Boundary, Landmark, and Route—to test long‑term memory and geospatial reasoning. The benchmark also introduces SwiftVLN, a modular framework for memory component experimentation, and demonstrates that models trained on satellite data can transfer to real‑flight UAV observations.
By Jiajun Jiang, Chunliang Hua, Zichun Chen, Yanxing Wu, Zeyuan Yang, Jie Song, Xiao Hu
The paper introduces VLN on the Fly, an onboard vision‑language navigation stack for aerial robots that separates grounding, planning, and control into inspectable stages. A quantized vision‑language model grounds instructions to a coarse image cell, depth estimation lifts this to a 3D goal, a fast B‑spline planner generates a feasible trajectory, and a pretrained reinforcement learning policy translates the trajectory into motor commands. In controlled indoor flights, the stack achieved the target in 13 of 15 trials with a mean goal error of 5.72 cm and 39.3% GPU utilization, and successfully tracked collision‑free trajectories in cluttered environments.
By Marco S. Tayar, Felipe Tommaselli, Gianluca Capezutto, Pedro Antonio Rabelo Saraiva, Pedro H. V. de Freitas, Lucas Kido, Guilherme Sonego, Ricardo V. Godoy, Marcelo Becker