How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows

NVIDIA Warp and MjWarp accelerate robotics simulation and learning workflows by leveraging GPU acceleration. While classic MuJoCo offers fast CPU-based simulation and can parallelize sampling across CPU cores, the increasing demands of learning workloads necessitate running multiple worlds simultaneously. GPU acceleration addresses this by enabling large batches of simulations to advance efficiently, keeping simulation and learning data close to the device. This approach significantly enhances the speed and scale of robotics development and testing.

This report details how NVIDIA Warp and MjWarp move robotics simulation from CPU-based parallel sampling to GPU-accelerated batch processing, unlike earlier methods focused on single-world performance.