QwenLM/Qwen-RobotNav

QwenLM/Qwen-RobotNav

Description: Official Repo for Qwen-RobotNav

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Created: 2026-06-29T19:06:24Z

Pushed: 2026-06-30T03:01:19Z

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README:

Welcome to the official repository of Qwen-RobotNav. Here, you can find official information about Qwen-RobotNav and post your questions (Issues).

> Note: There is currently no plan to release the model weights for Qwen-RobotManip or Qwen-RobotNav. We will continue adding report resources that can be publicly released to this repository.

🎬 Demo

If the video does not render in your browser, open the direct demo preview link. For the full-length high-resolution source, open big_agent.mp4.

Feature Highlights

This blog demo highlights the key design features of Qwen-RobotNav: unified multi-domain navigation, controllable observation context, agentic tool-call style execution, and zero-shot real-world deployment.

If the feature video does not render in your browser, open the direct feature preview link. For the full-length high-resolution source, open Nav_blog_demo.mov.

💡 Introduction

Qwen-RobotNav is a scalable navigation model built on Qwen3-VL. It unifies instruction following, point-goal and object-goal navigation, target tracking, autonomous driving, and embodied question answering under a shared waypoint-prediction interface.

The key idea is to treat navigation as context modeling. Different navigation tasks share a perception-planning backbone, but they require different strategies for consuming visual history: long-horizon instruction following needs memory, target tracking needs recent high-resolution frames, object search shifts between exploration and local approach, and driving depends on multi-view short-term motion context.

Qwen-RobotNav exposes this difference as a configurable observation protocol. An upper-level planner can call the same model with different task modes and context parameters, making Qwen-RobotNav a natural navigation primitive for agentic systems.

✨ Key Highlights

• 🧭 Unified Multi-Domain Navigation. One model covers VLN, PointNav/ObjectNav, target tracking, autonomous driving, and EQA.

• ⚙️ Controllable Observation Protocol. Token budget, temporal decay, per-camera weights, and frame sampling mode are inference-time controls.

• 🧠 Agentic Navigation Interface. The model can be called as a reconfigurable waypoint executor inside a two-tier planner with memory.

• 📈 Scalable Training. Qwen-RobotNav is trained on 15.6M samples with trajectory supervision and vision-language co-training, showing favorable scaling from 2B to 8B.

• 🌍 In-the-Wild Generalization. The model demonstrates zero-shot transfer to real-world robots and unseen environments.

🧠 Method

Qwen-RobotNav inherits the Qwen3-VL backbone and adds a lightweight 4-layer MLP action head. It outputs 8 waypoints, each parameterized as (x, y, theta), so diverse navigation tasks can be expressed as trajectory prediction under different prompts and observation configurations.

The core interface is a task-adaptive observation protocol with four main control axes:

• Visual token budget: total visual tokens shared across cameras and timesteps.
• Temporal decay: how strongly recent frames are favored over older observations.
• Camera weights: per-camera importance, such as emphasizing the forward view.
• Frame sampling mode: random sampling for broad history coverage or latest-frame sampling for recency.

During training, these parameters are randomized per sample. This lets Qwen-RobotNav accept new inference-time configurations without modifying the Qwen3-VL backbone or retraining a task-specific model.

Temporal order and camera identity are represented with natural-language tags interleaved with visual tokens, such as Time step 0 and Front View . This reuses Qwen3-VL's language space rather than adding custom time or viewpoint embeddings.

In the agentic system, an upper-level planner decomposes long-horizon goals into sub-goals, chooses the task mode, and sets the observation configuration for each call. RobotNav executes each segment as a reactive waypoint predictor, while compact trajectory summaries and persistent evidence memory keep the planner grounded over long episodes.

🏆 Benchmarks

Instruction Following (VLN-CE)

Qwen-RobotNav-8B achieves strong results on R2R and the longer-horizon RxR validation-unseen splits.

| Model | 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 Search and Tracking

| Benchmark | Model | Main Metric | Additional Metric | | :--- | :--- | :---: | :---: | | HM3Dv2 ObjectNav | Qwen-RobotNav-4B | 75.6 SR | 30.6 SPL | | EVT-Bench Tracking | Qwen-RobotNav-4B | 90.0 TR | 77.4 SR |

Embodied Question Answering

With the agentic navigation system, Qwen-RobotNav improves both exploration quality and answer accuracy.

| 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)

| Model | NC | DAC | TTC | Comfort | 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 |

Interface Ablation

The task-adaptive...