How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows
Hugging Face
MJWarp moved a MuJoCo robot pick-and-place workflow onto NVIDIA GPU simulation by porting a compatible MJCF model to batched Warp physics and validating that it matches the CPU setup. The walkthrough scales the same scene to 2,048 parallel MJWarp environments and advances them in one call to the step function on the GPU. The result is higher aggregate throughput (more world-steps per second) for reinforcement learning and large-scale sampling instead of focusing on single-environment step latency.
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