The Qwen family of foundation models already gives strong perception and reasoning about the physical world. But seeing is not acting: the gap between vision and language understanding and physical control remains the central bottleneck for embodied intelligence. The Qwen-Robot Suite bridges this gap with three foundation models — Qwen-RobotNav, Qwen-RobotManip, and Qwen-RobotWorld. Nav unifies five navigation task families through a controllable observation protocol. Manip turns heterogeneous robot data into a coherent canonical space, enabling cross-embodiment training at scale. World co-trains 20+ embodiments via a natural-language action interface under one world model. Together, they enable an agentic system where general intelligence translates directly into physical action.
The Qwen family of multimodal foundation models has made remarkable progress in understanding the physical world. Qwen-VL can parse complex spatial relationships, identify objects in cluttered scenes, follow multi-step visual instructions, and reason about physical configurations, giving physical agents a preliminary cognitive foundation. A VLM can already plan in language: “go to the kitchen, find the red cup, pick it up, and place it on the shelf.”
But understanding the physical world is not the same as acting in it. A VLM that can plan those steps cannot produce the motor commands that execute them. This is fundamentally an alignment challenge that language instructions and physical action signals live in different representation spaces, and bridging them requires more than perception alone. What makes this harder is that the embodied data needed to close this gap is fundamentally unlike internet text. It is heterogeneous by nature, expensive to collect, and narrow in diversity. A navigation trajectory, a tele-operated grasp, and a dashcam clip live in incompatible action spaces, observation formats, and embodiments. Naively pooling them produces conflict rather than synergy.
The Qwen-Robot Suite bridges this gap with three foundation models — Qwen-RobotNav, Qwen-RobotManip, and Qwen-RobotWorld — each aligning language with a different domain of physical action. We pursue generalization across language instructions and adherence to physical laws, and have achieved significant progress on both fronts. In this post, we also explore the potential of these models as low-level tools for building general-purpose agentic systems.
Each has its own technical report and deep-dive blog. This post tells the story of how they fit together.
NAVIGATE
Before an agent can manipulate anything, it has to get there. Mobile navigation spans tasks with fundamentally different memory requirements: instruction following demands long-horizon context while target tracking cares almost entirely about recent frames. No fixed observation strategy serves both.
Qwen-RobotNav, built on Qwen3-VL, addresses this through a parameterised navigation interface with two complementary dimensions: task modes that select the navigation behaviour (instruction following, object search, target tracking, autonomous driving), and controllable observation parameters (token budget, temporal decay, per-camera weights, frame sample mode) that govern how visual history is encoded. Trained on 15.6 million samples with co-training on vision-language data to preserve grounded perception, Qwen-RobotNav unifies five task families under a single set of weights. The parameterised interface also makes Qwen-RobotNav a natural building block for agentic systems. An upper-level planner (Qwen3.7-Plus) decomposes long-horizon 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. This extends the system to long-horizon reasoning with persistent memory, enabling it to solve complex user intents that require multi-step navigation, evidence gathering, and grounded response generation.
Highlights:
Benchmarking Qwen-RobotNav
Deployed zero-shot on a Unitree Go2 quadruped (NVIDIA Jetson Thor, 196ms latency) using only the built-in low-resolution camera. The robot executes step-by-step verbal instructions across multiple rooms in a previously unseen apartment.
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.
A single set of weights serves both legged robot navigation and autonomous driving. On NAVSIM closed-loop driving, Qwen-RobotNav-4B achieves 91.4 PDMS.
MANIPULATE
Physical agents need to interact with the real world — for example, completing manipulation tasks with robot arms. Yet an industrial arm on a production line and a service arm in a kitchen may perform visually similar grasping motions while having entirely different joint configurations and action spaces. The core challenge is making heterogeneous embodiments representationally compatible, so that scaling across robots and data sources produces synergy rather than conflict.
Qwen-RobotManip, built on Qwen3.5-4B VL with a flow-matching DiT action head, introduces three mechanisms to solve this. A unified 80-dimensional state-action representation is shared across single-arm, dual-arm, dexterous-hand, and mobile embodiments. Camera-frame end-effector delta pose actions make visually similar motions numerically proximate across robots, abstracting away morphological differences. In-context policy adaptation reads execution history as an implicit embodiment signature for on-the-fly adaptation.
Once the representation framework is unified, the data barrier drops. We train the VLA model on 11,320 hours of open-source robot data, 1,933 hours of open-source egocentric human video, and 24,808 hours of robot demonstrations across 15 embodiments synthesized from the human video via our Human-to-Robot synthesis pipeline — totaling >38,100 hours. Using only open-source data, the model already exhibits emergent generalization capabilities, including robustness to perturbations, zero-shot instruction following, reactive error recovery, and cross-embodiment transfer.
Highlights:
Key Finding — Alignment is the prerequisite for scale. Only models with unified cross-embodiment representations (UnifiedSpace + UnifiedEEF) exhibit clean log-linear data scaling. Without alignment, adding more data produces erratic or flat curves — scale cannot compensate for a broken formulation.
A single generalist policy handles complex manipulation across diverse task categories, scenes, and objects.
Following diverse unseen instructions across real-world settings (top row) and simulation (bottom row).
Tasks trained on other embodiments transfer zero-shot to new ones (top row); few-shot demonstrations enable rapid adaptation to entirely new tasks (bottom row).
IMAGINE
Real-world experience is the scarcest resource in robotics. Qwen-RobotWorld addresses this by learning the world’s state transition function directly: given the current observation and a natural-language action, it predicts what the world will look like next. The key design choice is expressing all actions in natural language — this converts end-effector poses, steering commands, and navigation waypoints into a single interface, enabling 20+ embodiment types and 500+ action categories to be co-trained under the Embodied World Knowledge corpus (8.6M video-text pairs, 200M+ frames). A 60-layer dual-stream MMDiT couples Qwen2.5-VL’s semantic representations with video latents. Using a full multimodal LLM as the action encoder — rather than a lightweight text encoder — is load-bearing: it brings internalized world knowledge that arms are rigid bodies, fluids spread, and objects fall, implicitly constraining generation toward physically plausible futures. Each domain reinforces the others: manipulation teaches contact physics, driving teaches 3D geometry, navigation teaches room-scale spatial reasoning.
Highlights:
Changing a single keyword — object, destination, or action verb — produces a correspondingly different future. The world model truly understands language, not just pattern-matches.
Given a single instruction, Qwen-RobotWorld generates temporally and spatially consistent videos across multiple camera viewpoints — critical for sim-to-real transfer and multi-camera policy training.
Given a human demonstration, Qwen-RobotWorld generates realistic robot execution across diverse embodiments — no teleoperation required.
Driving teaches large-scale 3D geometry and multi-agent dynamics; navigation teaches room-scale spatial reasoning. Each domain reinforces the others.
Each model is independently useful — but because all three expose language-first interfaces, general-purpose Qwen models can compose with them as physical-world tools, connecting general intelligence to physical action. We have an in-hourse project Qwen-RobotClaw, a robotics agent harness that allows Qwen VLM agents to call Qwen-Robot Suite models as physical-world tools while properly managing the context and memory required by long-horizon tasks, pushing physical intelligence toward more general and more complex real-world applications. Here are early examples of what this makes possible.
Qwen-Omni observes the scene, randomly proposes manipulation tasks via speech, and judges execution in real time. Each video shows Qwen-RobotManip completing tasks on the fly with no pre-defined task list — demonstrating that a general-purpose multimodal model can serve as the task proposer and evaluator, while the suite model handles physical execution.
We develop a VLM-driven agentic VLA system, in which the base Qwen-3.5 model serves as the high-level planner and Qwen-RobotManip handles low-level execution. Leveraging its capabilities in scene understanding, spatial reasoning, and task progress assessment, Qwen-3.5 decomposes a complex high-level instruction into a sequence of atomic subtasks, which are then executed by the VLA. This division of labor significantly improves robustness to OOD scenes and instructions.
We illustrate this with a table-cleaning task on a cluttered tabletop with a basket. Given such a fully OOD scene and abstract instruction, directly using the VLA model exhibits clearly abnormal behavior (right). With Qwen-3.5 as the planner, the system decomposes the task into fine-grained atomic subtasks in real time (left), allowing the VLA to focus on one simple step at a time and demonstrating compositional generalization.
We also observe that subtask decomposition helps the system recover from failure-and-retry loops. When the high-level VLM detects that execution has stalled, it replans by issuing a new subtask, enabling the system to resume progress and ultimately complete the task successfully.
By combining the agent system with Qwen-RobotNav, we achieve a substantial improvement over previous state of the art on long-horizon 3D physical-world exploration tasks, including Embodied Question Answering benchmarks such as HM-EQA, MT-HM3D, and EXPRESS-Bench. We can also deploy the same open-world exploration capability in real environments, as shown in the demos below. We will release more technical details in future updates.
In the first demo, the user asks the agent to find an open restroom in a real building. The agent scans the environment, follows corridor-level cues to search for restroom signage, discovers that the first restroom is unavailable due to a visible "Cleaning in Progress / 暂停使用" sign, and then replans to search for an alternative on the other side of the building. After verifying from visual evidence that the second restroom is open and accessible, it returns an evidence-grounded answer.
In the second demo, which was previously released together with Qwen3.7-Max, the agent calls Qwen-RobotNav to autonomously explore an open campus environment, correct its deviations along the way, and finally recover a lost umbrella.
We provide an experimental feature — Chat2Robot — where you can chat with a robot directly in your browser. Simply type a natural-language instruction, and watch the robot respond in real time. Give it a try and experience Qwen-Robot Suite in action!
Note: Chat2Robot currently supports Qwen-RobotManip only. The deployed policy is trained solely on the RoboTwin-Clean dataset, which contains only 50 tasks — it is not a perfect policy. Our goal here is to demonstrate a degree of zero-shot instruction following capability. This feature is still under active development and may not be fully polished — we welcome your feedback and suggestions!
We thank D-Robotics (Digua) for supporting this feature.
Physical world intelligence is still in its infancy. Contact-rich long-horizon tasks, lifelong learning, tighter integration between general-purpose planners and physical-world executors, and richer human-robot-environment interaction all remain open. But the path is becoming clear: start from strong multimodal understanding, bridge the vision-language representation space into each type of physical action, scale the training, and demand generalization.
A physical agent that can go anywhere, do anything, and foresee what comes next.
That is the destination — and the Qwen-Robot Suite is our first full step towards it.
@article{qwenrobotnav,
title={Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System},
author={Qwen Team},
year={2026}
}
@article{qwenrobotmanip,
title={Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models},
author={Qwen Team},
year={2026}
}
@article{qwenrobotworld,
title={Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation},
author={Qwen Team},
year={2026}
}Entering the Physical AI Era: Introducing the Qwen-Robot Suite
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