arXiv AI October 2, 2026 By Anqi Li, Zhiyong Wang, Jiazhao Zhang, Minghan Li, Yunpeng Qi, Zhibo Chen, Zhizheng Zhang, He Wang

UrbanVLA: A Vision-Language-Action Model for Urban Micromobility

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arXiv Machine Learning

Jul 15

APPLV: Adaptive Planner Parameter Learning from Vision-Language-Action Model

arXiv:2603. 08862v2 Announce Type: replace-cross Abstract: Autonomous navigation in highly constrained environments remains challenging for mobile robots.

By Yuanjie Lu, Beichen Wang, Zhengqi Wu, Yang Li, Xiaomin Lin, Chengzhi Mao, Xuesu Xiao

llms agents reinforcement-learning robotics fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jun 30

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.

By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem

llms agents diffusion robotics multimodal benchmarks safety

arXiv Machine Learning

Jun 17

Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned

arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.

By Maeva Guerrier, Karthik Soma, Jana Pavlasek, Giovanni Beltrame

llms diffusion robotics benchmarks

arXiv AI

6d ago

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

diffusion robotics multimodal

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.

agents reinforcement-learning robotics benchmarks safety

arXiv Computer Vision

Aug 28

UrbanGround: From Local Perception to Spatial Agency in a Real-Scale City

UrbanGround is a sandbox that tests how well multimodal large language model agents can translate local street‑view perception into reliable action within a physically realistic replica of Hong Kong. The platform offers closed‑loop first‑person interaction and an interactive map, allowing agents to navigate the 3D city and answer spatial questions. The study evaluates agents across three research questions—scene grounding, navigation over increasing distances, and robustness to route changes—revealing that while agents excel at visual recognition and short‑range reasoning, they struggle with sustained goal‑directed behavior and pedestrian‑aware movement.

By Tianjie Ju, Zheng Wu, Yueqing Sun, Yuhan Cui, Bobo Li, Shengqiong Wu, Pengzhou Cheng, Haodong Zhao, Zongru Wu, Xinbei Ma, Doris Zhang, Kunling Li, Mong-Li Lee, Wynne Hsu, Hao Fei, Qi Gu, Gongshen Liu, Zhuosheng Zhang

llms agents multimodal