{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"}],"enrichment":{"capability":"GPU-accelerated physics simulation for robotics using NVIDIA Warp, providing high-throughput MuJoCo-compatible simulation with batch rendering across parallel worlds.","skillfed_tags":["gpu-accelerated","physics-simulation","robotics"],"use_cases":["Train reinforcement learning policies for robotic control by running many parallel simulations on GPU.","Render training datasets for vision-based robotics by batch-rendering multiple camera views across simulation worlds.","Prototype robot behaviors and physics interactions during development using CPU mode before scaling to GPU.","Integrate differentiable physics into JAX-based machine learning pipelines via MJX.","Benchmark and profile physics simulation performance using the included mjwarp-testspeed tool with event tracing."],"what_it_does":"MuJoCo Warp is a GPU-accelerated physics simulator maintained by Google DeepMind and NVIDIA, built on top of the MuJoCo engine and NVIDIA's Warp compute framework. It brings high-throughput simulation to robotics research by offloading physics computation to NVIDIA GPUs, supporting rigid bodies, contacts, soft bodies, cloth, and signed distance fields. The simulator is designed as a near drop-in replacement for MuJoCo, with the same API surface minus a few unsupported features (IMPLICITFAST integrator, PGS solver, PLUGIN actuators, and experimental Flex support).\n\nThe package includes a high-throughput GPU batch renderer capable of rendering millions of frames per second across many parallel simulation worlds, with support for meshes, textures, heightfields, deformable bodies, heterogeneous multi-camera setups, and lighting. It integrates with JAX via MJX and with PyTorch through Isaac Lab and mjlab, making it suitable for robotics machine learning workflows. Installation is straightforward via pip, though an NVIDIA GPU is essential for production use; CPU mode is available for development and debugging.","worth_installing":"Yes, if you have an NVIDIA GPU and need high-throughput physics simulation for robotics research. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and offers a GPU-accelerated alternative to standard MuJoCo with batch rendering. Start with CPU mode for development if you lack GPU access. Not suitable for CPU-only environments requiring production performance."},"id":"mujoco-warp","links":{"html":"https://skillfed.io/packages/mujoco-warp","md":"https://skillfed.io/packages/mujoco-warp.md","pypi":"https://pypi.org/project/mujoco-warp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"mujoco-warp","python_support":"supports_current","summary":"MuJoCo Warp (MJWarp)"},"popularity":{"monthly_downloads":346204,"position":7358,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.11.0"}