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This guide walks through migrating an SO-101 robot arm simulation from standard CPU-based MuJoCo to MJWarp, which runs on NVIDIA GPUs. It batches 2,048 parallel environments using NVIDIA Warp kernels, allowing large-scale simulation steps without moving data off the device.

It is useful if you are scaling reinforcement learning workflows and need raw simulation throughput rather than single-step precision. The migration requires writing custom CUDA-centric logic and does not cover policy training, so it is strictly an infrastructure setup task for developers comfortable with GPU programming.

more: https://huggingface.co/blog/nvidia/how-to-use-nvidia-warp-and-mjwarp