At Foxglove, our team is intimately familiar with the challenges that come with robotics development. Existing tools – like data inspection applications, visualization programs, or debugging utilities – are often difficult to extend, clunky to use, and unsuitable for team collaboration, making for a painfully slow development workflow. We knew there was a better way to develop and debug our robots.

Our vision for Foxglove Studio is to ease these workflow pains and to accelerate robotics development for everyone. During our time working at Cruise, we experienced firsthand the problems that a fully integrated data visualization app could solve for a self-driving car company. We started Foxglove when we recognized the opportunity to build on top of this work and bring the same benefits to the wider robotics community.

Whether you’re building an autonomous car, a warehouse robot, or a self-driving rubber duck, we hope Foxglove Studio can help you spend less time fighting your tools, and more time focusing on what your robots are doing.

Why Foxglove Studio?

With Studio, our goal is to empower the robotics community to more easily make sense of their complex and noisy real-world data. We’ve taken the dizzying array of tools that robotics development usually requires, and integrated them into a single seamless developer experience.

Cross-platform

Foxglove Studio is conveniently packaged as a desktop app for Linux, Windows, and macOS. Making Studio a desktop application has unlocked some unique benefits. Because Studio is no longer limited by a sandboxed browser environment, it can connect directly to your ROS 1 stack via the native ROS connection. It also remains unconstrained by the performance restrictions that come with running huge amounts of robotics data through a browser.

Most ROS tools are only supported on Linux, but Foxglove Studio works cross-platform – even if your ROS stack is running on a different operating system, Foxglove Studio can communicate with your robot out-of-the-box.

Modular and extensible

Every view in Foxglove Studio is a customizable layout, made up of configurable data exploration tools called panels. Our panels can do everything from rendering interactive charts and 3D scenes, to displaying camera images and diagnostic logs. Using these modular panels, you can effectively build a custom robot control center from scratch to support your every possible workflow.

Here are some of our most commonly used panels:

3D panel – Display visualization markers (e.g. point clouds, bounding boxes, classification labels) in a 3D scene

Image panel – View images from your robot’s cameras

Plot panel – Plot values from incoming data in interactive charts

Raw Messages panel – Drill down into your incoming data

common panels

Most recently, we’ve added the following panels to tackle common robotics tasks:

Map panel – Display sensor\_msgs/NavSatFix messages as points on a map.

Parameters panel – Read and set rosparams from your connected ROS source.

Topic Graph panel – Render a graph showing the current ROS node and topic topology.

URDF Viewer panel – Use a Unified Robot Description Format (URDF) file to visualize your robot model.

new panels

Studio also provides a flexible Node Playground panel, where users can write custom data transformations in a code editor sandbox – to test out experimental outputs, or to create visualizations on the fly.

Open source

We’re partnering with our most important collaborators – our users – to build the future of Foxglove Studio.

Even with our best efforts to release and maintain tools that help every roboticist, we know a one-size-fit-all approach doesn’t cover every possible use case or meet every specific need. We don’t want Studio to limit your workflow options, or otherwise constrain how you can leverage our tools in the future.

That is why we’ll be implementing a public API for roboticists to build their own panels that encapsulate the project-specific functionality they need. This will go hand-in-hand with our extensions library, also in the works: in the same way Visual Studio Code has a rich ecosystem of plugins, we want Studio users to be able to browse a library of user-contributed extensions build to customize every aspect of the in-app experience. These changes will empower roboticists with any niche development needs to contribute their own custom tools that plug and play within the Studio ecosystem.

We’re also looking to integrate with new data formats beyond ROS 1. Please reach out with any data protocols or serialization formats you would like Studio to support.

Stay in touch

We hope this post helped introduce you to the world of Foxglove Studio. At Foxglove, our North Star is to streamline how you explore and extract insights from your robotics data. We hope Studio helps lower the barrier to entry for understanding your robot – on your journey from prototype to polished product.

We’re thrilled you’ve stumbled upon our community, and we hope you get involved! Let us know what you think is worth building next. Our codebase is written in TypeScript for easy contributing – feel free to open a pull request, or share your feature requests via our Slack concierge.

To see Studio in action, download the app and start tinkering!

Special thanks

We know Foxglove Studio could not exist without the hard work of our colleagues at Cruise, especially those building Webviz. We stand on the shoulders of giants, and we hope our own open source contributions help the robotics community rush towards the bleeding edge of our field.


As a scalable data management service, Foxglove Data Platform is a convenient one-stop shop for storing and analyzing your robotics data. You can streamline your data management process – usually an extremely time-consuming and expensive part of development – and reserve your team’s focus for less routine parts of your workflows.

Foxglove Data Platform currently supports importing and exporting ROS 1 (.bag) files and MCAP (.mcap) files. Due to upstream ROS 2 limitations, Data Platform cannot meaningfully store ROS 2 (.db3) files, as they do not contain the message definitions necessary for proper introspection and analysis.

This isn’t to say Foxglove Data Platform is off-limits for your ROS 2 team – there are several ways you can convert your .db3 files into self-contained MCAP files.

The easiest way to achieve this is to install our rosbag2\_storage\_mcap plugin, and have your robots record MCAP files directly. But if you want to stick with your existing recording plugin, or have already-recorded ROS 2 data waiting to be analyzed, continue along with this tutorial to convert your ROS 2 data into MCAP files for proper storage and analysis.

Install the mcap CLI

For a user-friendly way to convert your ROS 2 files, download the mcap CLI tool for your platform – we currently support Linux, Windows, and macOS.

Check out the releases page to download the latest release binary, or install directly via your command line using go:



Point to your custom message definitions

Next, you’ll need to specify to mcap where you store your ROS 2 message definitions.

Let’s say your msg files live in the /opt/ros/galactic directory. You’ll want to open up your setup bash file (i.e. /opt/ros/galactic/setup.bash) to set an environment variable called AMENT\_PREFIX\_PATH to your top-level ROS directory:



mcap will now know that it needs to search in /opt/ros/galactic to locate the ROS 2 message definitions on your hard drive, and to include those definitions when outputting your converted MCAP files.

Converting to MCAP

You’re now ready to convert your ROS 2 files into MCAP files!

Run the following command in your terminal to generate a MCAP file (e.g. my\_output.mcap) from a local ROS 2 file on your computer (e.g. your\_input.db3):



If you didn’t properly the AMENT\_PREFIX\_PATH environment variable in the previous step, you can even pass in your message definition directory paths as a command line flag:



If your message definitions live across multiple directories, use a colon-separated list of paths:



You should now be able to see a MCAP file output to your computer!

Run $ mcap info my\_output.mcap to see what your shiny new file actually contains – you should see a print out of your data's message count, time range, channels list, and other relevant information:



Congratulations on your first successful ROS 2 to MCAP conversion!

Uploading to Foxglove Data Platform

Now that you have your data in a format that Foxglove Data Platform understands, let’s get it imported so you can start tagging and analyzing your data with the rest of your team.

There are two ways to upload your data – through the Foxglove web console or with the foxglove-cli tool.

Web console

Create an account or sign in to your Foxglove account at console.foxglove.dev to view your dashboard.

Before uploading your MCAP to Foxglove Data Platform, you must add the device that your data should be associated with – i.e. the robot that your data was recorded on. If you haven’t yet created the necessary device, click “Add device” at the top of the Devices page to create a uniquely named robot for your team to track.

Foxglove Data Platform Devices view

Next, click "Import data" on the Files page to start uploading your my\_output.mcap file – be sure to associate it with your created device:

Foxglove Data Platform Files view

You should now be able to see your import on both the Files and Timeline pages! To see your data in the Timeline view, make sure you use the date picker to navigate to the time range in which your data was recorded:

Foxglove Data Platform Timeline view

foxglove-cli

Import your data with foxglove-cli if you want to write a script to automate or batch your imports. Install the latest release for your operating system and architecture, or install directly from source.

As with the web console, you must first authenticate yourself and add a device corresponding to your data:



To upload your output MCAP file, run the following command:



Alternatively, you can use the following shorthand to achieve the same results:



To verify that your import succeeded, print out a list of all your imports:



You should be able to locate an import with a device ID and filename that correspond to yours (i.e. dev\_drpLqjBZYUzus3gv and my\_output.mcap, respectively).

Continue to explore your data

Now that you’ve imported your ROS 2 data, you can leverage Foxglove Data Platform to better organize and analyze your team's data.

Attach rich metadata to your imported data, tag events of interest for your teammates, and submit hyper-specific queries to your data lake. Retrieve the information you need at lightning speed, with Data Platform's strategic data partitioning and indexing. You can even click a single button to stream a specific snippet of data into Foxglove Studio, for deeper visualization and debugging.

In short, this tutorial is just the start. Join us in our Slack channel if you have questions about getting started, or book a demo with our team to learn more about our active (and planned!) features.


If you're looking to get into robotics, there's no substitute for having a physical robot. It's the only way to get a feel for how sensors and actuators work together and to observe noisy real-world data coming in from sensors. Understanding how everything wires together is a fundamental part of the experience.

Fortunately, all-in-one robotics platforms are becoming increasingly accessible. If you want to focus on machine learning model training, Amazon's DeepRacer provides a ready-to-race autonomous RC car. A wide array of robotic arm kits are also available to purchase at newly reasonable prices. But for the novice roboticist looking for a complete robotics development experience, my current favorite is the Duckiebot.

What is Duckiebot?

Duckiebot

Duckiebot is unique, because it provides a deep dive into the real-world challenges and trade-offs that a roboticist would encounter, while still giving you the breadth of exposure to the entire end-to-end system. The Duckiebot kit consists of an embedded computer, a collection of sensors, and a 3D printed frame where you assemble everything by hand. The only tool required is a screwdriver (included in the kit), and you can build the robot in an afternoon.

To give your robot a place to drive, the Duckietown driving track kit comes with foam tiles and masking tape for arranging your tracks and personalizing your miniature town. Best of all, EdX launched a free online companion course (Self-Driving Cars with Duckietown) this year, taking you from a basic working knowledge of software development to a broad understanding of the disciplines involved in building robots.

Duckiebot tracks

One of the most fundamental challenges in robotics is system comprehension. Understanding what the robot is sensing (perception), what decisions it is making based on the perceived environment (planning), and how those decisions translate to actuator commands (controls) will be a core part of your development experience. For the Duckiebot, we have a few different tools at our disposal to start grappling with this comprehension challenge.

After you've assembled the hardware, flashed the SD card, and pressed the power button, you are rewarded with the first set of feedback: LEDs and a small display with system health metrics. The Duckiebot kit also provides a web interface with more health metrics and system status.

Duckiebot web UI

Getting to the next level of depth requires connecting directly to the robot's software. Like many robotics platforms, Duckiebot builds on top of the Robot Operating System (ROS) framework.

Though I won't go into a complete overview of ROS in this post, it's helpful to know that ROS-based robots are organized into modules called nodes. Nodes share information by publishing and subscribing to topics. Topics can be thought of as a stream of messages sharing a common data type, such as GPS coordinates, camera images, or steering angle commands.

To get a deeper look into what my Duckiebot is seeing or thinking, we need a tool that helps us inspect this underlying ROS data...

Visualizing ROS robots with Foxglove Studio

This is where Foxglove Studio comes in. Our team's long experience in robotics found that the existing tooling is often too rigid or limited to solve many common problems. Robotics tooling should be as accessible and easy to use as Unity3D is for game development, or as Photoshop is for photo editing.

Foxglove Studio uses a concept of panels, where different types of panels visualize information in different ways. These panels include camera feeds, time series plots, an extensible 3D view, a map, a connectivity graph, and more. You can arrange these panels into several dashboard views, or layouts, to investigate what your robot is doing.

Studio works on any platform (in my case, Windows), despite most ROS tools only supporting Linux. It also works out-of-the-box with ROS robots, so we can connect to Duckiebot (or any robot) by opening a new ROS connection to \<your-robot-host>.local - for me, this is darkwing.local.

Connecting with ROS

First, to inspect the published sensor data, let's add an Image panel to our layout to get a camera feed.

Image panel

Our duck is alive!

To get a high-level understanding of what software is running on the robot, you can add a Topic Graph panel, which shows ROS nodes and topics and how they are connected to each other.

Topic Graph panel

A lot is going on in the Duckiebot!

To start with a simplified overview, let's turn off the topics and services using the floating toolbar on the right.

Topic Graph panel, simplified

This view allows us to focus on what ROS nodes are currently running and how they connect to each other. Not all nodes are actively publishing data at the moment, since this is a brand new Duckiebot that hasn’t been fully developed into a self-driving robot yet.

Continue exploring

From here, you can add more panels and experiment with configuration options to see what other data you can visualize. As a first step, try using the Plot panel to create a time series plot of your Duckiebot’s time-of-flight sensor.

For a head start on experimentation, download this Foxglove Studio layout, which has been preconfigured with the following panels for easy Duckiebot analysis:

Data Source Info

Diagnostics – Summary

Image

Parameters

Plot

Log

Topic Graph

You can then import this layout file via the Layouts tab to load it into your Studio app.

Import Duckiebot layout

Congratulations on building and visualizing your first robot!

If you have any questions or ideas about robotics visualization, we'd love to chat with you in our Slack community.


Building and Visualizing Your First Robot

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