Fourier
Assembly Line
Fourier Trains Humanoid Robots for Real-World Roles Using NVIDIA Isaac Gym
Fourier, a Shanghai-based robotics company, is doing the heavy lifting by developing advanced humanoid robots that can be integrated into real-world applications where precision and agility are critical. The company announced the expansion of its GRx humanoid robot series with the launch of GR-2 in late September. Building on the previous-generation GR-1, the world’s first mass-produced humanoid robot, GR-2 features an upgraded hardware design, greater adaptability, advanced dexterity and a humanlike range of motion.
To develop and test GR-2, the Fourier team turned to NVIDIA Isaac Gym (now deprecated) for reinforcement learning. They are currently porting their workflows to the recently launched NVIDIA Isaac Lab, an open-source modular framework for robot learning designed to simplify how robots adapt to new skills.
While training GR-2 for the floor-to-stand maneuver, Fourier simulated the physical demands required for completing tasks at different levels of elevation. By replicating the GR-2 model, they tested how it performs under various settings and completed 3,000 iterations in around 15 hours, a notable reduction compared to traditional training methods. When transferred directly to GR-2’s physical controls, the model’s action tensors achieved an 89% success rate.