How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows
How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows Enterprise + Article Published September 23, 2026 Upvote 23 Johnny Nuñez Cano johnnynv nvidia Asier Arranz asiernvidia nvidia Rishabh Chadha rchadha-nv nvidia Ben Oliveri BenOliveri nvidia 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.
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- How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows Enterprise + Article Published September 23, 2026 Upvote 23 Johnny Nuñez Cano johnnynv nvidia Asier Arranz asiernvidia nvidia Rishabh Chadha rchadha-nv nvidia Ben Oliveri BenOliveri nvidia 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.
- MuJoCo Warp (MJWarp) , built on NVIDIA Warp , takes compatible MuJoCo models into that GPU-scale regime.
- In this article, we will move an SO-101 follower arm from a familiar MuJoCo workflow to as many as 2,048 parallel MJWarp environments and examine the technology and validation steps that make the transition possible.
- How MJWarp connects Python to GPU simulation.