--- title: X-Mobility canonical_url: "https://www.modelscope.cn/models/nv-community/X-Mobility" md_url: "https://www.modelscope.cn/models/nv-community/X-Mobility.md" repository: nv-community/X-Mobility last_updated: 2025-04-17 license: other library_name: - pytorch frameworks: - pytorch downloads: 1090 stars: 0 tags: - nvidia - x-mobility --- # X-Mobility > X-Mobility - nv-community 在 ModelScope 开源的模型。X-Mobility: End-To-End Generalizable Navigation via World Modeling nv-community/X-Mobility 是 ModelScope 魔搭社区上的机器学习模型，采用 other 许可。 - **Repository**: nv-community/X-Mobility - **License**: other - **Tags**: nvidia, x-mobility - **Downloads**: 1090 - **Stars**: 0 - **Last updated**: 2025-04-17 Source: https://www.modelscope.cn/models/nv-community/X-Mobility --- # **X-Mobility**: End-To-End Generalizable Navigation via World Modeling [**Code**](https://github.com/NVlabs/GR00T-Mobility-X-MOBILITY) | [**Paper**](https://arxiv.org/abs/2410.17491) ## Description: X-Mobility is an E2E generalizable navigation model using world modeling. It enables the robot navigation in an E2E manner by taking RGB image, robot state and (optional) route as inputs, then outputs the action commands for the robot directly. This model is ready for commercial/non-commercial use. ## License/Terms of Use NVIDIA Open Model License https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/. ## Model Architecture: **Architecture Type:** Vision Transformer, Multilayer Perceptron **Network Architecture:** X-Mobility introduces a learned state representation for robot navigation to capture relevant mobility features. It leverages a state estimation network with recurrent modules to handle non-Markovian effects, combining visual and robot state information for robust belief updates. A multi-task decoding framework enables RGB reconstruction and semantic segmentation, ensuring the latent state supports downstream tasks. The action policy network fuses the latent state with route encoding using self-attention, enabling effective action policy learning for linear and angular velocities, along with an optional navigation path for enhanced safety and control. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/67368ce09a7ebbd02789421e/uDu7r-893wLLmO8GkpCE5.png) ## Input: **Input Type(s):** Image, Robot state, Route **Input Format(s):** RGB, Floating points, Floating points **Input Parameters:** (2D, 1D, 1D) **Other Properties Related to Input:** Image size (width, height) being a multiplier of (5, 8) Robot states contains robot’s current linear velocity of size (1) Route is list of consecutive 2D segments from robot position towards the final destination of size (num_segments, 4) ## Output: **Output Type(s):** Actions **Output Format:** Floating points **Output Parameters:** (1D) **Other Properties Related to Output:** Actions consist of desired linear and angular velocities of size (6) ## Software Integration: **Runtime Engine(s):** TensorRT **Supported Hardware Microarchitecture Compatibility:** All of the below: * NVIDIA Ampere * NVIDIA Blackwell * NVIDIA Jetson * NVIDIA Hopper * NVIDIA Lovelace * NVIDIA Pascal * NVIDIA Turing * NVIDIA Volta * NVIDIA A100 **[Preferred/Supported] Operating System(s):** * Linux ## Model Version(s): This is the initial version of the model, version 1.0. ## Training, Testing, and Evaluation Datasets: ### Training Dataset: **Link:** https://huggingface.co/datasets/nvidia/X-Mobility **Data Collection Method by dataset:** Automated **Labeling Method by dataset:** Automated **Properties:** 100,000 total frames using Nav2 160,000 total frames using random actions Warehouse environment data Specific sensor type: Camera for images, wheel encoder for speed **Dataset License(s):** https://huggingface.co/datasets/nvidia/X-Mobility/blob/main/LICENSE ### Testing Dataset: **Link:** https://huggingface.co/datasets/nvidia/X-Mobility **Data Collection Method by dataset:** Automated **Labeling Method by dataset:** Automated **Properties:** 100,000 total frames using Nav2 160,000 total frames using random actions Warehouse environment data Specific sensor type: Camera for images, wheel encoder for speed **Dataset License(s):** https://huggingface.co/datasets/nvidia/X-Mobility/blob/main/LICENSE ### Evaluation Dataset: **Link:** https://huggingface.co/datasets/nvidia/X-Mobility **Data Collection Method by dataset:** Automated **Labeling Method by dataset:** Automated **Properties :** 100,000 total frames using Nav2 160,000 total frames using random actions Warehouse environment data Specific sensor type: Camera for images, wheel encoder for speed **Dataset License(s):** https://huggingface.co/datasets/nvidia/X-Mobility/blob/main/LICENSE ## Inference: **Engine:** Tensor(RT) **Test Hardware:** Jetson Orin ## Ethical Considerations : NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Model Limitations: This model is not tested or intended for use in mission critical applications that require functional safety. The use of the model in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms. Risk: Model underperformance in highly dynamic environments. Mitigation: Enhance dataset with dynamic obstacle scenarios and fine-tune models accordingly. Risk: Integration challenges in specific customer environments. Mitigation: Provide detailed integration guides and support, leveraging NVIDIA's ecosystem. Risk: Limited initial support for certain robot embodiments. Mitigation: Expand testing and validation across a wider range of robot platforms.