Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System
Authors: name: Jiazhao Zhang, name: Gengze Zhou, name: Hale Yin, name: Yiyang Huang, name: Zixing Lei, name: Qihang Peng, name: Haoqi Yuan, name: Jie Zhang, name: Xudong Guo, name: Xiaoyue Chen, name: An Yang, name: Fei Huang, name: Zhibo Yang, name: Junyang Lin, name: Dayiheng Liu, name: Jingren Zhou, name: Zhuoyuan Yu, name: Jingyang Fan, name: Zhixuan Liang, name: Pei Lin, name: Ye Wang, name: Anzhe Chen, name: Kun Yan, name: Xiao Xu, name: Jiahao Li, name: Lulu Hu, name: Minying Zhang, name: Shurui Li, name: Wenhu Xiao, name: Shuai Bai, name: Xuancheng Ren, name: Chenxu Lv, name: Chenfei Wu, name: Xiong-Hui Chen
Organization: Qwen

Agentic navigation systems require a base navigation model whose observation strategy can be externally reconfigured at inference time, because instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet demand fundamentally different strategies for consuming the visual stream. We present Qwen-RobotNav, a scalable navigation model built on Qwen-RobotNav that addresses it through a parameterised interface with two complementary dimensions: multiple task modes that select the navigation behaviour, and controllable observation parameters (e.g., token budget, per-camera weights) that govern how visual history is encoded. With training-time randomization over all parameters, Qwen-RobotNav is robust to any inference-time configuration requiring zero architectural modification to the Qwen-RobotNav backbone. We train Qwen-RobotNav on 15.6M samples; co-training with vision-language data prevents the collapse into reactive action-sequence mappers observed in trajectory-only training. The parameterised interface also makes Qwen-RobotNav a natural building block for agentic systems: for long-horizon scenarios, an upper-level planner decomposes goals into sub-tasks and dynamically switches Qwen-RobotNav's task mode and context strategy mid-episode, composing complex behaviours from repeated calls to the same model. Extensive experiments show that Qwen-RobotNav sets new state-of-the-art results across major navigation benchmarks. The model exhibits favourable scaling from 2B to 8B parameters, with joint multi-task training developing a shared spatial-planning substrate that transfers across task families, and demonstrates strong zero-shot generalisation to real-world robots across diverse environments.