# Qwen-RobotNav：面向智能体导航系统的可扩展导航模型 - 来源：Qwen：Blog Retrieval（API） - 作者：QwenTeam - 发布时间：2026-06-16 08:00 - AIHOT 分数：70 - AIHOT 链接：https://aihot.virxact.com/items/cmqg5kvq2006eslnc27k3juhc - 原文链接：https://qwen.ai/blog?id=qwen-robotnav ## AI 摘要 Qwen 发布 Qwen-RobotNav，基于 Qwen3-VL 在 15.6M 样本上训练，统一了视觉语言导航、目标导航、目标跟踪、自动驾驶和具身问答五个领域，无需修改架构即可在推理时切换任务模式和观察参数。模型在多项基准取得 SOTA：VLN-CE RxR 成功率 76.5%，HM3Dv2 目标导航 75.6%（仅 RGB），EVT-Bench 跟踪率 90.0%，NAVSIM PDMS 91.4，以及三项 EQA 新标杆。模型暴露四个可调轴（视觉 token 预算、时间衰减、相机权重、帧采样模式）。作为智能体系统的一部分，上层规划器 Qwen3.7-Plus 在 EXPRESS-Bench 上提升 15.4%，导航步数减少 77%。模型已零样本部署在 Unitree Go2 四足机器人上，无需环境微调。 ## 正文 Agentic navigation systems require a base navigation model with a configurable navigation context protocol: instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet demand fundamentally different context strategies for consuming the visual stream. Like the Model Context Protocol for LLM tool use, a navigation model needs a standardised interface to manage these diverse context requirements at inference time. We present Qwen-RobotNav, a scalable navigation model built on Qwen3-VL that addresses this through a parameterised interface with two complementary dimensions: task modes that select the navigation behaviour, and controllable observation parameters (token budget, temporal decay, per-camera weights) that govern how visual history is encoded. Trained on 15.6 million samples with training-time randomization over all parameters, Qwen-RobotNav generalizes to any inference-time configuration without architectural modification, unifying five task families under a single set of weights and serving as a natural building block for agentic systems. Highlights: 5 Domains 8 SOTAs One Model Unifies VLN, ObjNav, Tracking, Driving & EQA Context = Interface 4-Axis Observation Protocol Zero Architecture Change Agentic Navigation as a Tool Call Two-Level Memory Raises the new bar on EQA Generalization In-the-Wild Single-Camera Deployment in Unseen Environments Unified Multi-Domains Navigation: a single model with one set of weights achieves state-of-the-art across 5 navigation domains: 76.5% SR on VLN-CE RxR, 75.6% SR on HM3Dv2 object-goal (RGB only, surpassing depth-based methods), 90.0% tracking rate on EVT-Bench, 91.4 PDMS on NAVSIM, and new bests on 3 EQA benchmarks, with consistent scaling from 2B to 8B parameters. Controllable Observation Protocol: four axes (visual token budget, temporal decay, per-camera weighting, frame sample mode) are exposed as inference-time parameters and randomized per sample at training time, enabling any inference-time configuration without retraining or architectural modification. Agentic Navigation System: designed as a reconfigurable navigation primitive within a two-tier system, where an upper-level planner (Qwen3.7-Plus) decomposes long-horizon goals and dispatches configurable navigation calls while maintaining two-level memory, achieving +15.4% on EXPRESS-Bench with 77% fewer navigation steps over prior best. In-the-Wild Generalization: deployed zero-shot on a Unitree Go2 quadruped with its single low-resolution build-in camera, demonstrating strong generalization to in-the-wild environments and unconstrained natural-language instructions without any environment-specific fine-tuning. A Navigation Model with Controllable Context# Mobile navigation spans tasks with wildly different needs. Instruction following demands a long memory of past observations to re-reference distant landmarks. Target tracking cares almost entirely about the most recent few frames. Object search shifts mid-episode: broad history during exploration, tight recency during approach. Existing unified navigation models embed a single assumption about what to remember. Qwen-RobotNav addresses this by treating context as a first-class, externally controllable degree of freedom. The model provides four control axes that an agent can tune per call: Visual token budget: total tokens across all cameras and timesteps Temporal decay: how strongly recent frames are favored over older ones Camera weights: per-camera importance (the forward camera matters more than the rear) Frame sample mode: random for global history coverage, or latest for a tight recency window At training time, all these parameters are randomized per sample. The model never sees a fixed configuration, so it generalizes to any setting at inference time. Architecture. Qwen-RobotNav inherits the Qwen3-VL backbone and adds a lightweight 4-layer MLP action head that outputs 8 waypoints, each with position and heading. Camera identity and temporal order are communicated entirely through natural-language tags interleaved with visual tokens: Time step 0 Front View Front Right View ... Time step 1 Front View ... Built for Agentic Navigation# Qwen-RobotNav is designed as a reconfigurable navigation primitive within a two-tier system. An upper-level planner (Qwen3.7-Plus) decomposes long-horizon goals into sub-goals, while Qwen-RobotNav executes each navigation segment as a reactive waypoint predictor. The planner can dynamically switch Qwen-RobotNav’s task mode and context strategy mid-episode. The two tiers communicate entirely through natural language, keeping the system modular and extensible. Each navigation call specifies three things: a sub-goal instruction, a task mode (VLN / PointNav / ObjNav / Tracking), and an observation configuration. The same model weights serve all task phases; only the call arguments change. To support long-horizon reasoning, the system keeps a two-level memory. Each navigation segment produces a compact trajectory summary. A persistent evidence notebook accumulates durable conclusions across episodes (searched regions, candidate object locations, rejected hypotheses) so the planner always works with concise, relevant context. Training at Scale# Qwen-RobotNav is trained on 15.6 million samples across five task families, plus vision-language reasoning data that preserves the backbone’s perceptual grounding. Performance scales consistently from 2B to 8B parameters, with the most pronounced gains on long-horizon reasoning tasks. We also introduce an automated pipeline that converts text-to-video generations into navigation trajectories through prompt generation, video synthesis, VLM quality filtering, monocular depth estimation, and kinematic filtering, yielding 40K additional photorealistic samples without any 3D scene reconstruction. Instruction Following (VLN-CE) Development trend of VLN success rates on R2R and RxR validation-unseen splits. Traditional methods show steady early progress, while recent LLM/VLM-based approaches drive the latest performance gains, with Qwen-RobotNav achieving the highest success rates on both benchmarks. Qwen-RobotNav-8B achieves 72.1% SR on R2R and 76.5% SR on the longer-horizon RxR benchmark, surpassing prior best methods. Method R2R SR↑ R2R SPL↑ RxR SR↑ RxR SPL↑ NaVILA 54.0 49.0 49.3 44.0 NavFoM 61.7 55.3 64.4 56.2 ABot-N0 66.4 63.9 69.3 60.0 OmniNav 69.5 66.1 73.6 62.0 Qwen-RobotNav-4B 69.5 63.6 75.2 65.0 Qwen-RobotNav-8B 72.1 66.6 76.5 65.7 Object Searching On HM3Dv2 object-goal navigation, Qwen-RobotNav-4B achieves 75.6% SR using only RGB observations, surpassing all depth-based methods, and reaches only 1.72m from the goal on average. Method SR↑ SPL↑ VLFM 52.5 30.4 CogNav 72.5 26.2 Uni-NaVid 73.7 37.1 Qwen-RobotNav-4B 75.6 30.6 Qwen-RobotNav-8B 71.2 33.0 Active Visual Tracking On EVT-Bench, Qwen-RobotNav achieves the highest tracking rate at 90.0%, exceeding dedicated trackers and generalist models alike. Method TR↑ CR↓ SR↑ TrackVLA++ 81.0 2.10 86.0 NavFoM 80.5 — 85.0 ABot-N0 87.6 8.54 86.9 Qwen-RobotNav-4B 90.0 6.40 77.4 Qwen-RobotNav-8B 89.7 5.70 78.6 Embodied Question Answering Equipped with the agentic system, Qwen-RobotNav sets new state-of-the-art results across three EQA benchmarks, surpassing prior methods by substantial margins. Method HM-EQA Acc.↑ MT-EQA Acc.↑ EXPRESS LLM Score↑ Explore-EQA 58.4 36.2 — Memory-EQA 61.4 43.1 — FAST-EQA 69.2 50.5 68.7 Qwen3.5-Plus + QwenNav-8B 74.1 52.1 77.66 Qwen3.6-Plus + QwenNav-8B 76.7 54.4 79.27 Autonomous Driving (NAVSIM) On closed-loop driving evaluation, Qwen-RobotNav-4B achieves 91.4 PDMS, surpassing specialized driving models. Method NC↑ DAC↑ TTC↑ Comf.↑ EP↑ PDMS↑ NavFoM 97.7 93.5 92.3 100 79.6 84.3 AutoVLA 98.4 95.6 98.0 99.9 81.9 89.1 ReCogDrive 97.9 97.3 94.9 100 87.3 90.8 ReflectDrive 97.7 99.3 93.5 100 86.9 91.1 Qwen-RobotNav-4B 99.8 97.5 98.5 99.9 84.4 91.4 Qwen-RobotNav-8B 99.8 96.9 98.2 99.9 84.2 90.9 Qwen-RobotNav produces temporally consistent curved trajectories across both NAVSIM and the AlpaSim closed-loop simulator (zero-shot). Real-World Deployment# We deploy Qwen-RobotNav on a Unitree Go2 quadruped robot with on-device inference via NVIDIA Jetson Thor, achieving 196ms latency (5.1 Hz). The sole visual input is the Go2’s built-in low-resolution camera. All experiments are conducted zero-shot in previously unseen environments without any environment-specific fine-tuning. Deployment The robot executes navigation tasks in an apartment setting using step-by-step verbal instructions, traversing between the bedroom, living room, and bathroom while responding to fine-grained spatial directives. Instruction Following We evaluate a back-and-forth navigation task in an unseen exhibition hall: the robot first navigates 21.78 m from a living room to a hospital room following language instructions, then receives a reverse command and must precisely retrace the entire route. This is particularly challenging as it requires the model to maintain spatial awareness over long distances, ground diverse visual landmarks in both forward and reverse directions, and execute accurate bidirectional position control purely from language. Agentic Navigation In agentic mode, the system supports open-ended requests beyond simple route-following. Given the instruction "check whether a green umbrella was left at Cotti Coffee," the agent decomposes the task into sub-goals, navigates using corridor landmarks for localization, inspects the target scene, and produces an evidence-grounded answer without human intervention. Agentic navigation: autonomously checking for a green umbrella at Cotti Coffee. What’s Next# Qwen-RobotNav reframes multi-task navigation as a context modeling problem: diverse tasks share the same perception and planning backbone but require different strategies for consuming observations. Treating context as an externally controllable interface enables a single model to serve as a practical, deployable navigation primitive within agentic systems. ← Back to Qwen-Robot Suite @article{qwenrobotnav2026, title={Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System}, author={Qwen Team}, year={2026}}