{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"mjlab provides a GPU-accelerated reinforcement learning and robotics simulation framework combining Isaac Lab's manager-based API with MuJoCo Warp for composable environment design and training.","skillfed_tags":["robotics","gpu-simulation","reinforcement-learning"],"use_cases":["Train humanoid robots to follow velocity commands or track reference motions on flat or complex terrain","Run large-scale parallel simulations across multiple GPUs for faster policy learning","Evaluate trained policies in simulation before deployment using checkpoint loading from experiment tracking","Prototype new robot control tasks using composable environment building blocks and native MuJoCo data access","Conduct robotics research with reproducible, GPU-accelerated physics simulation and integrated logging"],"what_it_does":"mjlab is a reinforcement learning and robotics simulation framework that combines Isaac Lab's composable environment API with MuJoCo Warp, a GPU-accelerated physics engine. It provides building blocks for designing robot control tasks with direct access to native MuJoCo data structures, enabling efficient training of policies for humanoid robots and other agents on GPU clusters.\n\nThe framework is designed for research and production robot learning workflows. It supports multi-GPU distributed training, motion imitation from reference trajectories, velocity tracking, and policy evaluation. Users define tasks declaratively (e.g., 'Mjlab-Velocity-Flat-Unitree-G1') and train agents using built-in RL algorithms, with integration to Weights & Biases for experiment tracking and checkpoint management.","worth_installing":"Yes, if you are conducting GPU-accelerated robot learning research or building production robot control systems. The active maintenance, production-stable status, permissive license, and integration with standard ML tools (PyTorch, Weights & Biases, TensorBoard) make it a solid choice. The large dependency footprint and GPU requirement are expected for this use case. Not suitable for CPU-only or lightweight simulation needs."},"id":"mjlab","links":{"html":"https://skillfed.io/packages/mjlab","md":"https://skillfed.io/packages/mjlab.md","pypi":"https://pypi.org/project/mjlab/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-09","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"mjlab","python_support":"supports_current","summary":"Isaac Lab API, powered by MuJoCo-Warp, for RL and robotics research."},"popularity":{"monthly_downloads":97579,"position":13145,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.0"}