How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows
Basic usage: structs, batch sizes, and a minimal step Performance tuning Workflow to migrate a MuJoCo scene to MjWarp Get started What’s next References Classic MuJoCo provides fast CPU-based robot simulation for developing, testing, and controlling robots and it can parallelize sampling across CPU cores. But as learning workloads grow, the question shifts from how quickly one world can run to how many worlds can run at once. GPU acceleration makes it possible to advance those worlds in large batches while keeping simulation and learning data close to the device.
- ▪Basic usage: structs, batch sizes, and a minimal step Performance tuning Workflow to migrate a MuJoCo scene to MjWarp Get started What’s next References Classic MuJoCo provides fast CPU-based robot simulation for developing, testing, and co
- ▪But as learning workloads grow, the question shifts from how quickly one world can run to how many worlds can run at once.
- ▪GPU acceleration makes it possible to advance those worlds in large batches while keeping simulation and learning data close to the device.
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Back to Articles How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows Enterprise + Article Published September 23, 2026 Upvote - Johnny Nuñez Cano johnnynv Follow nvidia Asier Arranz asiernvidia Follow nvidia Rishabh Chadha rchadha-nv Follow nvidia Ben Oliveri BenOliveri Follow nvidia Putting it together Start with one useful Warp Kernel Three properties make this useful in robotics: Differentiability and Determinism. What is MuJoCo Warp (MJWarp)? Basic usage: structs, batch sizes, and a minimal step Performance tuning Workflow to migrate a MuJoCo scene to MjWarp Get started What’s next References Classic MuJoCo provides fast CPU-based robot simulation for developing, testing, and controlling robots and it can parallelize sampling across CPU cores.
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