InItroduction
In recent years, quadruped robots have gained significant attention in smart manufacturing, security inspection, and scientific research and education. Compared with wheeled or tracked robots, their main advantage lies in their ability to flexibly adapt to varying terrains. However, enabling quadruped robots to truly operate in diverse and complex environments has remained a persistent challenge in the industry. Against this backdrop, NVIDIA’s release of the X-Mobility model provides a promising breakthrough. By integrating this model into our quadruped robot platform and combining it with our in-house system integration expertise, we have achieved enhanced mobility intelligence.
What is NVIDIA X-Mobility?
NVIDIA’s X-Mobility (End-to-End Generalizable Navigation via World Modeling), released on Hugging Face, is an end-to-end model designed for navigation and motion control. Its primary objective is to enable robots to maintain stable and generalizable mobility across diverse environments.
Figure 1. Semantic segmentation of obstacles within a custom-designed virtual environment.
Technical Features
Fig. 2. Semantic fitting during training, where green denotes navigable regions.
Why We Chose X-Mobility
In the past, quadruped robots primarily relied on predefined gaits and traditional controllers (such as MPC or PID). While this approach proved reliable in structured environments, it often performed poorly in dynamic and unknown environments.
X-Mobility offers several key advantages:
Our Integration & Experiments
We deployed X-Mobility on our quadruped robot platform and conducted multi-scenario testing:
Fig. 3. Semantic segmentation of corridor images performed by the model, where green denotes navigable regions.
Real-world office scenario: The robot is capable of planning paths in an office environment and autonomously avoiding desks, chairs, and pedestrians.
Fig. 4. Obstacle avoidance test performed in a real-world environment.
Looking Ahead
To further enhance this integration, we will also leverage NVIDIA’s ReMEmbR. ReMEmbR allows robots to retrieve, store, and utilize past experiences in real-time. By combining ReMEmbR with X-Mobility and VLMs, quadruped robots will be able to not only understand natural language commands but also recall relevant contextual knowledge and past navigation experiences. This capability is essential for robust long-term autonomy in dynamic industrial environments.
In the long term, this convergence of X-Mobility, VLMs, and ReMEmbR will drive the future of smart factories, AI-powered robotics, and automated logistics—positioning quadruped robots as true working partners on the factory floor.
Conclusion
The adoption of NVIDIA X-Mobility has transformed our quadruped robot from a “demonstration platform” into a productive tool. This represents not only a technological breakthrough but also an important milestone in advancing intelligent manufacturing to its next stage.
Nvidia blog link: https://developer.nvidia.com/blog/streamline-robot-learning-with-whole-body-control-and-enhanced-teleoperation-in-nvidia-isaac-lab-2-3/