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X-Mobility: End-To-End Generalizable Navigation via World Modeling
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.
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
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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.