UrbanVLA is a Vision‑Language‑Action framework designed to enable delivery robots to navigate large‑scale urban environments using long‑horizon route instructions. The model aligns noisy route waypoints with visual observations and plans trajectories, trained through a two‑stage pipeline of supervised fine‑tuning on simulated data and reinforcement fine‑tuning on mixed simulation and real‑world data. Experiments show UrbanVLA outperforms strong baselines by over 55% on the SocialNav task and demonstrates reliable real‑world navigation in large urban settings.
By Anqi Li, Zhiyong Wang, Jiazhao Zhang, Minghan Li, Yunpeng Qi, Zhibo Chen, Zhizheng Zhang, He Wang
arXiv:2509.19571v2 Announce Type: replace-cross
Abstract: Designing or learning robot policies that generalize zero-shot across a range of language instructions and objects is a core problem in robot...
By Sacha Morin, Kumaraditya Gupta, Mahtab Sandhu, Charlie Gauthier, Francesco Argenziano, Kirsty Ellis, Liam Paull
arXiv:2608. 14047v1 Announce Type: cross Abstract: This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART).
By Yi Ding, Yanzhao Yu, Xili Dai, Xianbiao Qi, Peiwen Sun, Xueqian Wang, Xiangyu Yue, Jianan Wang
arXiv:2608.30935v1 Announce Type: cross
Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embod...
By Shaoan Wang, Aocheng Luo, Fei Huang, Jingyi Xu, Xiaoyang Wang, Yueyu Wang, Qianli Ma, Fan Yang, Ran Mei, Jia Wei, Jiangpeng Hu, Xuhao Liu, Hongming Chen, Yuanbin Shao, Yiyang Lin, Ziliang Li, Liang Pan, Xinhang Liu, Yuntao Ma, Tingxiang 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:2607. 10991v1 Announce Type: cross Abstract: As mobile robots become more integrated into everyday human environments, social robot navigation is becoming essential for ensuring human comfort, safety, and trust.
By Ali Ahmadi, Hamed Rahimi, Adrien Jacquet Cretides, Marie Samson, Mahdi Khoramshahi, Mohamed Chetouani
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
arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.
By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
By Puzhen Yuan, Angyuan Ma, Yunchao Yao, Huaxiu Yao, Masayoshi Tomizuka, Mingyu Ding
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.
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
arXiv:2609.39915v1 Announce Type: new
Abstract: Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents...
By Haoxiang Shi, Zaijing Li, Muhe Ding, Xiang Deng, Yaowei Wang, Liqiang Nie