Running ROS2 on Apple Silicon: Native, Docker, or VM? Here's What Actually Works

👋 Intro: It Started With “Let Me Just Install ROS2…

I’ve been working in robotics for a while, mostly on Linux machines, but after recently switching full-time to a MacBook with an M2 chip, I figured I’d set up a quick ROS2 + Gazebo environment and get to work.

How hard could it be, right?

Well… what I expected to be a one-afternoon setup turned into a surprisingly long journey across native installs, Docker hacks, VMs, display issues, and a lot of dead ends.

The good news? I eventually found a setup that works surprisingly well.
The bad news? It took trying a lot of things first.

This blog isn’t a tutorial. It’s a story. One that might sound familiar if you’ve ever tried to get ROS2 running on macOS. I’ll share what I tried, what broke, what almost worked, and what finally gave me a setup that feels smooth, stable, and usable.

🔍 Spoiler Alert: Here’s What I Tried (and What This Blog Covers)

(Just here for the easiest way to run ROS 2 on macOS? Feel free to jump to the final setup

✅ Native installation
🖥 Virtual machines
🐳 Docker containers
🎯 What finally worked for me

If you’re on a Mac and thinking about diving into ROS2, this might save you a few headaches (and maybe a weekend or two).

🧪 Native Install: So Close, Yet So Fragile

I started where most people probably would: trying to run ROS2 natively on macOS.

The official ROS2 Jazzy documentation [1] does provide a macOS-specific guide. It outlines how to install ROS2 from source using Homebrew and Python 3.10, and to its credit, it’s much more up-to-date than older ROS docs, while it doesn’t explicitly mention Apple Silicon support, some components are actively being patched for compatibility, and users have reported success using workarounds [2].

So I gave it a shot.

And for core development tasks, launching nodes, debugging, even opening RViz2, it actually worked. In fact, native performance felt fast. Everything from build times to message passing ran really smoothly.

But once I tried stepping beyond the basics, things started to unravel:

GUI tools like rqt or qt_gui_cpp didn’t always launch cleanly
Some Qt/Python mismatches caused crashes or missing widgets
Gazebo support (especially Ignition/Garden) was mostly absent unless built from source, and even then, flaky

In particular, PySide-based tools often failed to initialize properly. I ran into widget rendering issues, mismatched Qt plugin errors, and the occasional unexplained crash, all common when mixing Homebrew, Python 3.10+, and Qt5 on macOS.

That said, I’ve personally never been a big fan of installing large, multi-package systems like ROS2 directly into my macOS environment. Between conflicting Python versions, Homebrew dependencies, and how tightly ROS2 integrates with system paths, it always feels a bit fragile, like one macOS update or brew upgrade could break everything.

So while native installation is useful for quick testing or CLI-based development, I started looking for a way to keep things more isolated, and that’s where RoboStack came in.

RoboStack is a Conda-based ROS2 distribution for macOS and Apple Silicon to avoid having to manage everything myself. RoboStack made installation a lot easier [3]., especially for getting RViz and the core ROS2 tooling up and running.

For lightweight development and basic visualization, this setup was great. But anything simulation-heavy or graphics-dependent (like Gazebo) still ran into limitations mostly due to missing GPU support and lack of native Ignition binaries for macOS ARM.

If you’re curious and want to try the native route yourself, I’d recommend:

Just know that while it’s doable, not every package will “just work” and things may break depending on macOS or Python versions.

🖥 Virtual Machines: The Compatibility Tradeoff

Next I tried putting ROS2 in its native habitat - Ubuntu.

🧭 Parallels Desktop: super smooth setup, full Ubuntu experience. Gazebo even launched, and RViz mostly worked without any major issues. GPU performance was surprisingly decent thanks to Parallels’ virtualization layer. That said, since Parallels is a paid product (around $100/year), it may not be the most budget-friendly choice for everyone.
⚙️ UTM (QEMU): great performance for CPU tasks, completely open-source, and easy to configure. While it lacks hardware-accelerated graphics, making tools like Gazebo noticeably sluggish or unreliable, it remains one of the most accessible ways to run ROS 2 on macOS in a stable, Ubuntu-based environment. In fact, the official ROS 2 documentation recommends using UTM for simulation workflows, particularly with Webots [webots_ros2], as a practical workaround for the native compatibility issues that plague many GUI tools. If you want to try UTM, this setup guide is a good starting point.
🧪 VMware Fusion (Tech Preview): promising OpenGL support (4.3!) and evolving quickly, but rendering glitches and refresh bugs made it unreliable for day-to-day simulation.

Virtualization helped in one sense, giving me a full Ubuntu environment with proper ROS2 binaries, but the moment I needed hardware-accelerated 3D, the experience fell apart.

🐳 Docker: Clean, Fast, Headless… and GUI-Challenged

Docker was probably my smoothest experience overall - at least for running nodes.

I could:

Build and test ROS2 packages
Use tools like ros2 topic, ros2 service, ros2 launch
Connect to hardware or even remote topics for debugging

Once I got the environment right, it was clean, reproducible, and fast exactly what you want during development.

Of course, Docker does come with its own quirks. If you’re new to it, things like volume mounts, networking, or setting up docker-compose might feel a bit much at first. But honestly, if you’re working in robotics or devops-adjacent areas, learning Docker is almost inevitable and totally worth it.

The one major hurdle? GUI tools.

XQuartz never behaved reliably on my M2 Mac
Qt-based apps crashed or threw cryptic plugin errors
RViz wouldn’t connect to the display, even after trying all the usual tricks: setting $DISPLAY, running the container with —privileged, sharing XQuartz, and every X11-forwarding workaround I could Google.

So while Docker was great for core ROS2 development, anything graphical was still a struggle, until I tried something a little more unconventional…

🎯 The Workaround That Worked: Docker + VNC

I stumbled across docker-ros2-desktop-vnc, a prebuilt Docker image that runs a full Linux desktop inside the container, exposes it over VNC, and lets you connect via browser using noVNC.

This flipped the whole experience:

Rendering stays in the container
No more OpenGL/X11 dramas on the host
Just open a browser and boom - RViz, Gazebo, and friends

It’s not blazing fast, you’re running software rendering after all but for development, testing, and even demos, it was absolutely good enough. And more importantly consistent. I stopped spending time fixing the environment and got back to building robots.

If you want to try it yourself, it’s just one command:

docker run -p 6080:80 --security-opt seccomp=unconfined --shm-size=512m ghcr.io/tiryoh/ros2-desktop-vnc:humble

Then open your browser at http://127.0.0.1:6080

Inside the browser-based desktop, you can launch your GUI tools directly from the built-in terminal:

ros2 run rviz2 rviz2
ros2 launch ros_gz_sim gz_sim.launch.py gz_args:="-r shapes.sdf"

That’s it. Nothing fancy — just a setup that works.

After all the trial and error, this was the first time everything felt smooth again. If you’re on Apple Silicon and just want to focus on building robots not debugging GUIs, this setup might just be your new favorite tool.

🧭 Takeaways

It took a while, but here’s what I learned:

Native macOS installs are getting better, but still fragile
VMs are helpful, but GPU limitations are real
Docker is cleanest for ROS2 dev but GUIs need workarounds
VNC-based Docker containers are a surprisingly good middle ground

If you’re on a Mac, you can run ROS2 with RViz and even Gazebo, you just need to shift how you think about running GUIs.