How to Use NVIDIA Warp and MjWarp to Accelerate Robotics Simulation and Learning Workflows

Classic MuJoCo provides fast CPU-based robot simulation for developing, testing, and controlling robots and it can parallelize sampling across CPU cores. But as learning workloads grow, the question shifts from how quickly one world can run to how many worlds can run at once. GPU acceleration makes it possible to advance those worlds in large batches while keeping simulation and learning data close to the device.

MuJoCo Warp (MJWarp), built on NVIDIA Warp, takes compatible MuJoCo models into that GPU-scale regime. In this article, we will move an SO-101 follower arm from a familiar MuJoCo workflow to as many as 2,048 parallel MJWarp environments and examine the technology and validation steps that make the transition possible.

Figure 1. How MJWarp connects Python to GPU simulation. MuJoCo loads and compiles the MJCF model; MJWarp implements the physics in NVIDIA Warp, which compiles CUDA kernels to advance simulation states on NVIDIA GPUs.

This is the second article in our State of Simulation for Physical AI series. The first article mapped the robot-simulation landscape. Here, we prepare and scale the simulation environment; we do not train a policy. The later Newton and Isaac Lab installments cover the next integration layers.

Putting it together

Layer	Role in the stack
NVIDIA Warp	Python kernel language: single instruction, multiple threads (SIMT), autodiff, PyTorch/JAX interop
MJWarp	MuJoCo physics on Warp: same MJCF, batched GPU throughput
Your scene (SO-101)	Familiar Menagerie / Robot Studio assets + task geometry
Next (Newton / Isaac Lab)	Multi-solver API, USD, sensors, managers, training loops

Decision shortcut:

If you need…	Reach for…
Single-robot MPC / teleop	MuJoCo CPU
Max throughput on raw MuJoCo physics	MJWarp (or mjlab)
JAX training recipes	MuJoCo Playground / MJX (impl='warp')
Multi-solver + Isaac Lab integration	Newton — next post in this series

Start with one useful Warp Kernel

The three value propositions of Warp are: