Choosing a robotics simulator in 2026

The single most common question I get from new roboticists: "Which simulator should I use?" There isn't one answer. There are six serious simulators in 2026, and each one is the best choice for something different. Here's how to pick.

Thirty-second decision tree

• Training RL policies for a quadruped or arm? → MuJoCo (MJX)
• Building a ROS 2 stack and want a sim that plugs in? → Gazebo Harmonic
• Need photorealistic perception and scale to thousands of envs? → Isaac Sim / Isaac Lab
• Beginner-friendly, want a sim that pip-installs? → PyBullet
• Doing serious control theory, optimization, or grasp planning? → Drake
• Teaching a classroom, need Windows support and a GUI? → Webots

If you already know what you're optimizing for, stop here and pick. If not, read on — the details matter.

MuJoCo (and MJX)

Open-source since 2021, DeepMind's maintained. MuJoCo is the RL researcher's default for contact-rich rigid-body physics: manipulators, quadrupeds, humanoids. The 2022 port MJX runs the same physics on JAX, batched on GPU/TPU — thousands of parallel environments, 10,000× sim speedup over CPU.

• Strengths: fast, accurate, great contacts, Python-first, MJX batches brilliantly for RL.
• Weaknesses: no photorealism (camera renders are functional, not pretty); no native ROS bridge (people write their own); less ecosystem for perception pipelines.
• Use it when: you want to train a policy and deploy it. This is the default for hobbyist RL in 2026.

Gazebo (Harmonic)

The longtime ROS companion. The 2024 merge of Classic Gazebo and Ignition into "Gazebo Harmonic" consolidated the lineage; the current Gazebo is actively maintained by Open Robotics.

• Strengths: native ROS 2 integration via ros_gz_bridge; realistic sensor plugins; big library of existing world models; works on large outdoor worlds.
• Weaknesses: slower than MuJoCo for contact-rich sim; steeper learning curve; the plugin system is powerful but confusing.
• Use it when: your stack is ROS 2 and you need the sim to look and behave like the real robot running ROS 2.

NVIDIA Isaac Sim / Isaac Lab

Built on Omniverse; shipped free to developers since 2021. Isaac Sim is the full photorealistic sim; Isaac Lab (2024 release, succeeding Isaac Gym and OmniIsaacGym) is the thin RL-focused layer on top.

• Strengths: photorealistic rendering (path-traced or rasterized); massive parallel env count on a single GPU; first-class sensors (RTX lidar, IMU, camera); USD workflow is the cleanest asset pipeline in robotics.
• Weaknesses: NVIDIA GPU required; heavy install; steep learning curve; Omniverse changes every year.
• Use it when: you need photorealism for perception training, or you're scaling policy training to thousands of environments per GPU.

PyBullet

Erwin Coumans's friendly option. pip install pybullet and you're rolling. Used by the OpenAI robotics team for years, now mostly a teaching tool.

• Strengths: no install pain; great docs and tutorials; URDF support out of the box; PyBullet Gym wrappers get you into RL fast.
• Weaknesses: development has slowed; contacts less accurate than MuJoCo; not GPU-parallelized.
• Use it when: you're teaching, prototyping, or you've got a laptop without a GPU and want to start today.

Drake

MIT's answer from Russ Tedrake's group. Drake is less "simulator" and more "robotics research toolkit that happens to include a simulator." Used heavily for trajectory optimization, motion planning, and grasp analysis.

• Strengths: rigorous continuous-time physics; first-class optimization (Mathematical Program API, MOSEK, Gurobi, SNOPT); Lagrangian/Hamiltonian formulations; mature Python and C++ APIs.
• Weaknesses: smaller community than MuJoCo or Isaac; no photorealism; fewer turnkey RL examples; Bazel build system is its own adventure.
• Use it when: you're doing control-theoretic research, trajectory optimization with hard constraints, or taking MIT's Underactuated Robotics course.

Webots

Cyberbotics's classroom favorite. Cross-platform, GUI-first, and free since 2018.

• Strengths: runs on Windows/Mac/Linux with a proper GUI; deep sample library of world files; widely used in schools.
• Weaknesses: research community has largely moved on; physics less sophisticated than MuJoCo or Drake; slower for large-scale training.
• Use it when: you're building a course and need students on mixed OSes to all have the same sim open.

Honorable mentions

• CoppeliaSim (formerly V-REP) — commercial, European university staple; solid but eclipsed by MuJoCo + Isaac.
• MATLAB Simulink + Robotics System Toolbox — if you already live in MATLAB, it's fine; most new users should not start here.
• SAPIEN — photorealistic manipulation benchmark sim; narrower than Isaac but well-regarded.
• Genesis (2024) — new multi-physics sim from CMU/Stanford; watch this one, not yet a mainstream default.

What changed since 2024

• MJX matured into a real production option; many labs switched off CPU MuJoCo to JAX/MJX for scale.
• Isaac Lab replaced Isaac Gym and OmniIsaacGym; the RL API is now finally stable.
• Gazebo Classic was retired — everyone's on Harmonic now.
• Drake got a clean Python-only install option (no more Bazel for most users).
• Hugging Face LeRobot made it easy to push learned policies to multiple sims with a common wrapper.

My default recommendation for a new learner

If you're starting from zero in 2026 and want to learn broadly:

1. Start in PyBullet for the first week — the zero-install path keeps you moving.
2. Move to MuJoCo (MJX) as soon as you touch RL — the speedup is worth the transition.
3. Add Gazebo when you start building with ROS 2 — that's just how the ecosystem works.
4. Reach for Isaac or Drake only when you hit the specific needs they solve.

Don't pick one and marry it. Every serious roboticist uses two or three simulators — the skill is knowing which is right for the current question.