Contents
Figure 1: One robot, four simulators, and about forty contradictory forum threads — the real cost is the weekend you spend guessing
So you're trying to pick a physics simulator for your robotics project, and every forum thread you read contradicts the last one. Sound familiar? I've burned way too many weekends benchmarking these three, so let's cut through the noise together.
Quick heads up before we start: one of these three tools has actually been discontinued. I'll explain exactly what that means for you and what to use instead, because pretending otherwise would be doing you a disservice.
By the end of this comparison you'll know which simulator matches your actual workload, why the "GPU-parallel training" row matters more than any feature checklist, and which of these tools is quietly frozen in time. The goal is to make this decision once, not to rewrite a project three months in like I did. If you're newer to the robotics side of this series, the RL for games and robotics guide is the useful primer before the simulator debate starts mattering.
The Real Question: What Are You Actually Building?
Before comparing feature lists, figure out what you need. A simple grasping demo has completely different requirements than training a locomotion policy across thousands of parallel environments. These tools were built for different priorities, and picking based on hype instead of your actual use case is how projects stall.
I learned this the hard way on a manipulation project where I picked a simulator because it was trendy, not because it fit. Cost me weeks of rewriting. Don't be me.
- If your day involves grasping an object with a robot arm, contact fidelity and a stable grasp are the whole game.
- If your day involves training a quadruped to walk, throughput matters: more environment steps per hour means a policy that converges before your patience runs out.
- If your day involves thousands of parallel environments on a GPU, the CPU-bound tools drop out of the conversation entirely, whether or not their benchmarks look impressive.
Write that requirement down first. Everything else in this article is downstream of that one sentence.
MuJoCo: The Precision Workhorse
MuJoCo (Multi-Joint dynamics with Contact) has built its reputation on accurate contact dynamics and stable simulation of complex articulated systems. DeepMind acquired it and made it free and open-source, which honestly changed the entire landscape overnight.
Why People Love It
- Exceptional contact physics: handles complex contact scenarios (grasping, locomotion) with genuine precision
- Fast simulation speed: optimized C code means high-frequency stepping without melting your CPU
- MJX for GPU acceleration: JAX-based MuJoCo XLA lets you run massively parallel simulations on GPU
- Strong research adoption: widely used in academic robotics and RL research papers
I've used MuJoCo for locomotion research, and the contact stability genuinely impressed me. IMO, if your project involves anything with delicate contact dynamics — legged robots, dexterous manipulation — MuJoCo handles it more gracefully than alternatives.
The activity level backs this up too: releases 3.12, 3.13 and 3.14 all shipped in 2026 alone, on a roughly monthly cadence. That is not the release rhythm of a tool anyone is quietly abandoning.
Where It Falls Short
- Steeper learning curve: the XML-based MJCF format takes time to get comfortable with
- Smaller asset ecosystem compared to some competitors, though this keeps improving
- Free now, but historically licensed: older tutorials and forum posts still reference the paid version, which confuses newcomers
That last one is worth a direct warning — you will find decade-old posts telling you MuJoCo costs money, and they are simply out of date. pip install mujoco is the entire story now, and the MuJoCo tutorial in this series walks through the setup and a first PPO training run.
PyBullet: The Accessible Generalist
PyBullet built its popularity on being easy to pick up. It wraps the Bullet physics engine in a friendly Python API, and for years it's been the default starting point for people learning robotics simulation.
Here's the thing though — PyBullet's development pace has slowed considerably. The last tagged release is 3.25 from April 2022, and commits have become infrequent since. It's still functional and widely used in tutorials, but if you check its maintenance activity, it's clearly not receiving the active development that MuJoCo or NVIDIA's tools get. That doesn't mean don't use it. It means know what you're signing up for.
Strengths Worth Knowing
- Gentle learning curve: straightforward Python API, minimal boilerplate to get started
- URDF and SDF support: loads robot models without much friction
- Built-in inverse kinematics: convenient for manipulation tasks without external libraries
- Huge tutorial ecosystem: years of educational content make troubleshooting easier
Real Limitations
- Slower development pace: updates and bug fixes come far less frequently these days
- CPU-bound simulation: no native GPU-parallelized environments like the newer tools offer
- Less suited for large-scale RL training: struggles when you need thousands of parallel environments
I still recommend PyBullet for beginners and small-scale prototyping. Ever tried explaining URDF parsing to someone new to robotics? PyBullet makes that first step way less painful than jumping straight into MuJoCo's XML format. The full walkthrough is in the PyBullet tutorial — GUI versus DIRECT mode, loading models, and a Gymnasium environment end to end.
Isaac Gym: Here's Where Things Get Complicated
Okay, important update if you're researching this in 2026: Isaac Gym is officially deprecated. NVIDIA's own Isaac Lab documentation states that both the Isaac Gym Preview Release and IsaacGymEnvs are now deprecated, and directs everyone toward Isaac Lab, a newer framework built on top of Isaac Sim. NVIDIA even ran a GTC 2026 session dedicated to walking users through that migration.
This matters a lot if you're starting a new project. Building on deprecated software means no bug fixes, no updates, and a shrinking support community.
What Isaac Gym Offered (Historically)
- GPU-accelerated tensor API: massive parallel environment simulation directly on GPU
- URDF and MJCF import with automatic mesh decomposition
- Domain randomization built into its RL environments: useful for sim-to-real transfer research
What Replaces It: Isaac Lab
- Built on Isaac Sim: more realistic rendering and physics via NVIDIA's Omniverse platform
- Actively maintained: regular updates — the 3.0 early-access release shipped in September 2026 — unlike its predecessor
- Modular RL framework: designed specifically for robot learning workflows, including official migration guides from old IsaacGymEnvs code
If you've got existing Isaac Gym research code, migrating isn't trivial — several developers have complained publicly about how much effort the transition takes, and the change list explains why: quaternion conventions flip from xyzw to wxyz, joint ordering changes from depth-first to breadth-first, and YAML task configs become Python configclass definitions. But starting fresh? Isaac Lab is genuinely the better long-term bet. Don't build new infrastructure on something NVIDIA has already stopped supporting :/
If you want to understand where that GPU-native workflow came from in the first place, the Isaac Gym tutorial covers it — including the same "start with Isaac Lab" conclusion.
Side-by-Side Comparison
| Simulator | Best For | Biggest Drawback |
|---|---|---|
| MuJoCo | Precise contact dynamics, research-grade accuracy | Steeper learning curve |
| PyBullet | Beginners, quick prototyping, tutorials | Slower ongoing development |
| Isaac Gym (deprecated) | Legacy projects only | No longer supported |
| Isaac Lab | Large-scale GPU-parallel RL training | Requires learning the Omniverse ecosystem |
GPU-Parallel Training: Where the Real Differences Show
If your project needs thousands of parallel environments for RL training, your options narrow fast. MuJoCo's MJX brings genuine GPU-parallel capability through JAX, and Isaac Lab offers similar scale through Omniverse's architecture.
- Recall the Stable-Baselines3 article's vectorized-environment idea directly — the same principle, pushed much further: the more environments you step at once, the more environment steps you buy per hour of wall-clock time.
- PyBullet, meanwhile, wasn't built with this kind of scale in mind. Training a policy across 4,000 parallel environments simply isn't PyBullet's strength, and forcing it there usually means fighting the tool instead of your actual research problem.
- GPU-parallel simulation is also where hardware choices start biting — a simulator that keeps thousands of environments resident on device wants real VRAM, and a mismatch there shows up as slow step rates rather than a clean error message.
If large-scale GPU training is a core requirement rather than a nice-to-have, the field is effectively down to two options: MuJoCo with MJX, or Isaac Lab.
A Practical Decision Framework
Let me save you some research time with straightforward guidance:
- Just starting out in robotics simulation? Start with PyBullet. The learning curve is forgiving, and tutorials are everywhere — and once you want hardware to point it at, the robotics kits guide covers that half of the setup.
- Need research-grade contact physics for locomotion or manipulation? Go with MuJoCo. Its physics accuracy is hard to beat.
- Training RL policies at massive scale on GPU? Choose Isaac Lab, not Isaac Gym. Building on deprecated software is asking for pain later.
- Maintaining existing Isaac Gym research code? You can keep using it short-term, but start planning your Isaac Lab migration now rather than later.
Common Mistakes People Make
Picking a simulator because it is trendy rather than because it fits
Recall the manipulation rewrite from this article's opening directly — simulator choice is cheap to reverse in week one and brutally expensive to reverse in month three.
Starting new infrastructure on deprecated Isaac Gym
Recall NVIDIA's own deprecation wording directly — "deprecated" means no fixes and a shrinking community, which is exactly the wrong foundation for work you plan to maintain.
Forcing thousands of parallel environments into a CPU-bound tool
Recall the GPU-parallel section directly — PyBullet was never designed for that scale, so the fight is with the tool, not with your research problem.
Assuming PyBullet is unusable because development slowed
Recall the release history directly — a stable API with infrequent commits still ships working prototypes; slow development costs you future features, not today's experiment.
Choosing from blog benchmark numbers instead of your own robot model
Recall this article's own premise directly — published speed figures use someone else's asset, mesh complexity, and contact setup; the only benchmark that predicts your project is a run on your robot, in your scene.
Recommended Books
- Modern Robotics: Mechanics, Planning, and Control by Kevin M. Lynch and Frank C. Park — the mechanics underneath every one of these engines, which is what actually explains why their contact behavior differs.
- Robot Modeling and Control by Mark W. Spong, Seth Hutchinson and M. Vidyasagar — kinematics and control theory for reading a URDF and knowing whether the model in front of you is sane.
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto — the training side of the equation, since half of this decision is really "how fast can I collect useful experience."
Want the simulator-to-trained-policy pipeline in runnable code? Grab the GPTAstra full course at https://cutt.ly/5yviN6qd — it walks from loading a robot model in a simulator to training and evaluating a policy with vectorized environments.
Frequently Asked Questions
Which robotics simulator should I choose?
It depends on what you are building. MuJoCo for precise contact dynamics and research-grade accuracy, PyBullet for beginners and quick prototyping, and Isaac Lab — not Isaac Gym — for large-scale GPU-parallel reinforcement learning training.
Is Isaac Gym discontinued?
Isaac Gym is officially deprecated, not deleted. NVIDIA's Isaac Lab documentation states that both the Isaac Gym Preview Release and IsaacGymEnvs are now deprecated, and it provides migration guides. The download still works for legacy work, but new development happens in Isaac Lab.
Is PyBullet dead or still worth learning?
Still worth learning. The last tagged PyBullet release is 3.25 from April 2022 and commits have become infrequent, but the API is stable, the tutorials still work, and it remains one of the least painful ways to load a URDF and see a robot move today.
Is MuJoCo free to use?
Yes. DeepMind acquired MuJoCo in 2021 and open-sourced it in 2022. It installs with pip install mujoco, has no license keys, and is actively released — versions 3.12, 3.13 and 3.14 all shipped in 2026 alone.
How hard is it to migrate from Isaac Gym to Isaac Lab?
Non-trivial but documented. Beyond renamed APIs, the quaternion convention changes from xyzw to wxyz, joint ordering changes from depth-first to breadth-first, and YAML task configs become Python configclass definitions. Budget real migration time rather than expecting a find-and-replace.
Which simulator handles thousands of parallel RL environments best?
Isaac Lab through NVIDIA's Omniverse stack, with MuJoCo's JAX-based MJX as the other genuine GPU-parallel option. PyBullet is CPU-bound and was never designed for thousands of parallel environments, so forcing it there costs more time than it saves.
Wrapping This Up
Picking a robotics simulator isn't about which one has the flashiest marketing — it's about matching the tool to your actual technical needs. MuJoCo wins on physics precision, PyBullet wins on accessibility, and Isaac Lab wins on large-scale GPU training now that Isaac Gym has been retired.
Will you eventually need more than one of these across different projects? Probably, yeah — most robotics researchers end up fluent in at least two. But for now, pick based on what you're actually building today, not what sounds impressive in a README. Worth noting this decision also feeds the rest of this series: whatever you pick here ends up in the sim-to-real transfer conversation sooner or later, and a simulator that can't step fast enough quietly caps every reward design experiment you run inside it.
Now go load the same simple robot model into PyBullet and MuJoCo on your own machine and time 1,000 simulation steps in each — the kind of ten-minute check the hardware kits article applies to physical kits, done instead with software. That single number, measured on your robot and your scene rather than someone else's benchmark, is worth more than every feature table on the internet — including this one.