arXiv AI By Huaide Jiang, Yash Chaudhary, Yuping Wang, Zehao Wang, Raghav Sharma, Manan Mehta, Yang Zhou, Lichao Sun, Zhiwen Fan, Zhengzhong Tu, Jiachen Li

NavTrust: Benchmarking Trustworthiness for Embodied Navigation

Read the original on arXiv AI →

arXiv:2603. 19229v2 Announce Type: replace-cross Abstract: There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agents navigate to a specified target object.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 28

NavGen: Visual Generative Models as a Scalable Data Engine for Embodied 3D Navigation

NavGen introduces a text-to-video data generation pipeline that creates about 400K vision‑language navigation episodes for both indoor and outdoor scenes, using high‑fidelity visual generative models. The approach includes a style‑diversification method to scale up rare, hard‑to‑collect data. Models trained on NavGen data outperform those trained on existing UAV navigation datasets and achieve a 75% success rate in real‑world flying experiments.

By Xijie Huang, Yongyang Wan, Chengbin Dong, Zimo Ding, Mo Zhu, Yijin Wang, Zhiyang Liu, Fei Gao, Yuze Wu, Xin Zhou
Hugging Face Trending Papers
Jul 22

NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation

Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored.

arXiv AI
Jul 14

A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation

arXiv:2607. 09792v1 Announce Type: cross Abstract: Navigation is a fundamental capability of autonomous systems, yet most existing approaches rely on highly structured models and strong prior assumptions, limiting their robustness in open and uncertain real-world environments.

By Liuyi Wang, Kai Sheng, Zongtao He, Jinlong Li, Yongrui Qin, Haojie Dai, Xiangyi Wang, Jingwei Yang, Qingqing Yan, Chengju Liu, Qijun Chen
arXiv Machine Learning
Sep 30

IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking

arXiv:2606.08729v2 Announce Type: replace-cross Abstract: Developing navigation policies requires simulation scenarios that support repeatable training and evaluation. Despite the availability of num...

By Ruihua Han, Shuai Wang, Chengyang Li, Rui Gao, Xinyi Wang, Zhe Liu, Guoliang Li, Yupu Lu, Qi Hao, Jia Pan, Hengshuang Zhao