The central problem in building physical AI is data. Large language models train on internet-scale text, but robots must learn from physical interaction: what happens when a cup slips, a cable bends, or a gripper contacts an object at the wrong angle. Collecting this data in the real world is slow, expensive, and sometimes destructive. Simulation has become the bridge.
What started as a tool for debugging geometry and testing controllers has evolved into a core part of the model development loop. Teams now use simulation to generate perception datasets, train reinforcement learning policies, collect demonstrations, augment real-world data, benchmark models, and test against rare or adversarial scenarios. The question is no longer whether to simulate, but which engine to use and how to integrate it into the broader stack.
The Simulation Stack
A typical physical AI workflow involves two computers: a GPU workstation or cluster running the simulation engine, and an edge device executing the trained policy during deployment. The simulation side handles physics, rendering, sensor modeling, and data generation at scale. The edge device runs the distilled policy in the real world.
The landscape of engines has fragmented by use case. For humanoid robots and dexterous manipulation, the most prominent options include MuJoCo, MuJoCo Warp, NVIDIA Isaac Sim, Isaac Lab, and the newer Newton physics engine. Each occupies a different position on the trade-off between physical accuracy, visual fidelity, and throughput.
MuJoCo and MuJoCo Warp
MuJoCo, short for Multi-Joint dynamics with Contact, is an open-source physics engine built around precise dynamics, contact-rich motion, and model-based optimization. It is deterministic, offers well-defined inverse dynamics, and uses generalized coordinates. These properties make it a favorite for control research and policy validation where physical correctness matters more than visual realism.
MuJoCo Warp (MJWarp) is a GPU-accelerated reimplementation written in NVIDIA Warp. It brings MuJoCo-style physics into a batched GPU setting, simulating many worlds in parallel to reduce CPU-GPU transfer bottlenecks. It sacrifices some of MuJoCo's single-step precision for throughput, making it better suited for large-scale reinforcement learning.
Isaac Sim and Isaac Lab
Isaac Sim is NVIDIA's robotics simulation framework built on Omniverse. It provides high-fidelity physics through PhysX, photorealistic RTX rendering, and sensor simulation for cameras, depth sensors, lidar, and radar. It can ingest assets from CAD, URDF, MJCF, and real-world reconstruction pipelines, allowing robots to be tested in realistic digital-twin environments.
Isaac Lab is a GPU-accelerated framework specifically for robot learning at scale. With version 3.0.0, Isaac Lab decoupled from Isaac Sim and Omniverse, becoming a lightweight multi-backend framework. Developers can choose Isaac Sim with PhysX and RTX for sensor-rich workflows, or Newton physics for high-throughput headless simulation, with photoreal sensors available through a standalone connector.
Newton Physics Engine
Newton is the newest entry, an open-source GPU-accelerated physics engine developed by NVIDIA, Google DeepMind, and Disney Research through the Linux Foundation. Built on NVIDIA Warp and OpenUSD, it integrates MuJoCo Warp as a key backend and provides multiple solver implementations rather than prescribing one numerical method. Newton supports generalized coordinates (MuJoCo, MuJoCo-Warp), maximal coordinates (PhysX, Jolt, Brax), and specialized solvers for particles, cloth, and soft bodies.
In the broader ecosystem, Newton acts as a modern physics layer that Isaac Lab and MuJoCo Playground can build on, giving researchers a path toward scalable, differentiable, simulator-agnostic physics.
The Bigger Picture
An unusual amount of this infrastructure is being built in the open. Physics engines, differentiable simulators, and training pipelines are emerging under open-source licenses with open governance, lowering barriers to entry and accelerating iteration. This is a meaningful shift from even a few years ago, when much of the simulation stack was proprietary or research-only.
The ecosystem is fragmenting and recombining into a layered stack where different tools specialize and interoperate. No single engine dominates every use case. MuJoCo remains the gold standard for rigorous numerics. Isaac Sim leads on photorealism and sensor simulation. Newton is positioning itself as the flexible, multi-backend foundation. The right choice depends on whether the priority is control accuracy, visual fidelity, or training throughput.
What is clear is that simulation is no longer a peripheral tool in robotics. It is where policies are stress-tested, where data is amplified, and increasingly, where entire training pipelines begin. As models move from static datasets into physical interaction, the quality and scale of simulated experience is becoming a direct determinant of what robots can learn to do.