The primary challenge in building physical AI systems is data availability. Unlike LLMs and VLMs that can be trained on internet-scale datasets, robotics lacks this advantage. A robot must learn the consequences of interacting with the physical world, but collecting real-world data is slow, expensive, and risky. Simulation provides a bridge, enabling developers to generate large amounts of photorealistic, physically grounded data at a fraction of the cost.
Earlier robotics simulators were used primarily to debug geometry or test controllers. Today, simulation is part of the model development loop. Teams use it to generate perception datasets, train reinforcement learning policies, collect demonstrations, augment real-world data, benchmark models, and test policies against rare scenarios. This shift is why industrial labs and academic groups are increasingly contributing to simulation engines.
Several simulation engines are available, each targeting different use cases: humanoid robots, aerial vehicles, ground robots, autonomous vehicles, quadrupeds, and dexterous manipulators. Popular engines include MuJoCo, MuJoCo Warp, NVIDIA Isaac Sim, and Isaac Lab.
MuJoCo (Multi-Joint Dynamics with Contact) is a fast, accurate, open-source physics engine designed for robotics, biomechanics, and reinforcement learning. It emphasizes precise dynamics, contact-rich motion, and model-based optimization, making it ideal for systems where physical correctness matters.
MuJoCo Warp is a GPU-accelerated implementation of MuJoCo written in NVIDIA Warp. It brings MuJoCo-style physics into a batched GPU setting, making it well-suited for reinforcement learning and large-scale policy training. It simulates many worlds in parallel, reducing CPU-GPU transfer bottlenecks.
NVIDIA Isaac Sim is an open-source robotics simulation framework built on NVIDIA Omniverse. It provides high-fidelity physics through PhysX, photorealistic RTX rendering, and robotics-focused sensor simulation for cameras, depth, lidar, and radar. It supports synthetic data generation and digital-twin environments.
Isaac Lab is an open-source, GPU-accelerated, agent-ready simulation framework for robot learning. With version 3.0.0, Isaac Lab has decoupled from Isaac Sim, becoming a lightweight, multi-backend framework. Developers can choose to use Isaac Sim with PhysX and RTX rendering for photorealistic workflows or run lightweight headless Newton physics for high-throughput simulation.
Newton is an open-source, GPU-accelerated, extensible, and differentiable physics engine developed by NVIDIA, Google DeepMind, and Disney Research. Built on NVIDIA Warp and OpenUSD, it integrates MuJoCo Warp as a key physics backend. Newton provides multiple solver implementations, including generalized coordinates for articulated rigid-body systems and maximal-coordinate formulations for others. It targets robot learning frameworks like Isaac Lab and MuJoCo Playground.
The simulation ecosystem is fragmenting and recomposing into a layered stack, with different tools specializing and interoperating. A growing share of core infrastructure is emerging in the open source, with open governance and GPU-accessible tooling lowering the barrier to entry. This shift is foundational for anyone building in Physical AI.