Cross-platform robotics simulation โ replaces Isaac Sim/Lab
Replaces DGX Spark: Isaac Sim / Isaac Lab
roboticssimulation
Basic idea
MuJoCo (Multi-Joint dynamics with Contact) is a physics simulation engine developed by DeepMind and now used across robotics research at Google, OpenAI, Meta, and hundreds of academic labs. It simulates rigid body dynamics, contact forces, and actuators at high speed with smooth, differentiable physics โ the differentiability is key to why it dominates reinforcement learning research.
MuJoCo MJX is a JAX-based version of the same simulation that runs on GPUs. On Apple Silicon, it runs via jax-metal on the Metal GPU, achieving 650,000 simulation steps per second on M3 Max. MuJoCo Playground (RSS 2025 Outstanding Demo Paper Award) provides ready-made RL environments for locomotion and manipulation tasks with proven zero-shot sim-to-real transfer โ you train a policy in simulation and deploy it to a physical robot without any adaptation.
Isaac Sim requires NVIDIA GPUs and cannot run on macOS. MuJoCo is the practical alternative for Mac-based robotics research.
What you'll accomplish
MuJoCo installed and the 3D interactive viewer working. A Python script running the humanoid model at 650K+ steps/sec using MJX on the Metal GPU. A brief reinforcement learning training run with MuJoCo Playground on the Go2 quadruped locomotion task, with training curves showing reward improvement.
What to know before starting
Rigid body dynamics: MuJoCo numerically integrates Newton's equations for systems of connected rigid bodies. Each timestep solves for contact forces, joint torques, and resulting accelerations.
Differentiable contact: MuJoCo's contact model produces smooth gradients through contact events โ essential for gradient-based RL algorithms that backpropagate through simulation.
MJX: The JAX-compiled version of MuJoCo. Running `jax.jit(mjx.step)` compiles the physics step to a Metal GPU shader. `jax.vmap` vectorizes it across thousands of parallel environments in a single Metal dispatch.
Sim-to-real transfer: A policy trained in MuJoCo simulation and deployed directly to a physical robot without any real-world data or fine-tuning. MuJoCo Playground environments are tuned to minimize the sim-to-real gap.
PPO (Proximal Policy Optimization): The RL algorithm used in Playground. It collects experience from many parallel environments, computes policy gradient updates with a clipped objective function that prevents large policy updates.
Prerequisites
โข macOS 12.0+, Apple Silicon (M1 or later)
โข Python 3.9+
โข jax-metal installed (see JAX playbook for setup)
โข XQuartz for the 3D viewer (optional): `brew install --cask xquartz`
Time & risk
Duration:: 30 minutes
Risk level:: Low โ pip installs only, no system modification