paper / kevin_zakka / May 13
MuJoCo Playground is a fully open-source robot learning framework built on MJX that integrates a physics engine, batch renderer, and training environments into a unified stack. It enables researchers to train policies in minutes on a single GPU via a simple pip install, supporting quadrupeds, humanoids, dexterous hands, and robotic arms. The framework achieves zero-shot sim-to-real transfer from both state and pixel inputs, significantly lowering the barrier to entry for robotics research.
roboticsreinforcement-learningsim-to-realmujocorobot-simulationopen-source-frameworkgpu-training
“MuJoCo Playground enables policy training in minutes on a single GPU.”
paper / kevin_zakka / May 13
mjlab is an open-source robot learning framework that pairs MuJoCo Warp's GPU-accelerated physics backend with Isaac Lab's manager-based, composable environment API — while drastically reducing setup friction to a single install command with minimal dependencies. By exposing native MuJoCo data structures directly, it avoids the abstraction overhead common in heavier frameworks like Isaac Lab or MuJoCo MJX wrappers. The framework ships with reference tasks covering velocity tracking, motion imitation, and manipulation, positioning it as a practical bridge between research-grade flexibility and production-grade GPU throughput.
robot-learninggpu-accelerationsimulation-frameworkreinforcement-learningmujocoopen-sourcerobotics
“mjlab can be installed with a single command and requires minimal dependencies, unlike comparable GPU robot simulation frameworks.”