About the Robotics Field Guide
What the Robotics Field Guide is, who it is for, how its pages are made and checked, and how it stays independent.
The Robotics Field Guide is an independent reference for people who build robots. It covers the whole stack: frames and kinematics, dynamics and control, actuators and sensors, state estimation and mapping, planning, manipulation and mobile robots, ROS 2 and simulation, robot learning, and the safety standards and industry around them. The aim is a page you can open in the middle of a job and trust: the maths that matters, code that runs, and versions and specifications with a date on them.
Who it is for
- Engineers who already work on robots and want a reference that is not tied to one vendor’s documentation or one framework’s tutorials.
- Software, electronics and mechanical engineers moving into robotics, who need the robotics-specific parts without a second degree.
- Students and self-taught builders who want a structured path from a first transform to a robot running in simulation, and then on hardware.
We assume you can write a short Python program and remember some linear algebra and calculus. We do not assume you have used ROS or a simulator before.
How it is organised
| Section | What it holds |
|---|---|
| Learn | Ordered lessons in four tracks (beginner, intermediate, advanced, expert), each with diagrams, runnable code and an exercise |
| Topics | One hub per subject, from foundations to careers, that gathers the lessons, reference pages, projects, news and papers on it |
| Platforms | One page per platform or maker, such as ROS, NVIDIA, MuJoCo, Universal Robots, Franka, Unitree and drone autopilots, with tooling, licensing and the sharp edges |
| Reference | Cheat sheets, a software matrix, a glossary and a robot database, each with a “last verified” date |
| Projects | Builds that run end to end in simulation on a laptop, with notes on moving them to hardware |
| News | Short briefs on releases and industry events, each linked to its sources and to a page that teaches the background |
| Research | A weekly digest of robotics papers in plain language, saying whether each result was in simulation or on a physical robot |
| Events | Conferences, courses and community meetings with dates and costs |
How it is made
Content is drafted with AI assistance, from sources that are read and summarised in new words, and every piece passes automated originality, fact-grounding and code-execution checks before a person reviews it.
Lessons are written, then reviewed before they go live. Their code is run in the same environment readers use: Python blocks in a pinned set of libraries on a laptop CPU, and ROS 2 blocks in the ROS 2 Jazzy container. A block that does not run does not ship.
Evergreen pages (topic hubs, platform pages, reference pages and projects) are reviewed before publication. Every version, price, specification and citation on them is checked against its source when the page is written, and the “last verified” date records when that happened.
News briefs are drafted daily from the sources they link to. Before a brief publishes, an automated guard checks it. The guard rejects text that copies its sources too closely, and it runs every code block. Every number, date and claim in a brief must appear in a listed source. When a claim is a maker’s rather than a measured fact, the brief says so.
We write about robots in a particular way. A demonstration video is not a product on sale, and a product on sale is not a fleet in service. Figures from a launch announcement are reported as the maker’s figures, with the source named. Where we do not know something, we say so.
Independence
The Robotics Field Guide takes no sponsorship from robot makers, software vendors or anyone else, and carries no affiliate links. Product and company names are trademarks of their owners and appear here only to identify the products.
When we criticise a tool, a robot or a claim, it is because we found a specific problem, and we try to say exactly what and where it is.
Sister sites
The Robotics Field Guide is built on the same templates and rules as two sister sites. Where subjects meet, the sites link to each other rather than repeat each other.
- AI/ML Field Guide, for machine learning background: neural networks, training, evaluation and deployment.
- FPGA Guide, for programmable logic, including signal and image processing in hardware.
Errors and contact
If something on a page is wrong, the corrections page explains how to tell us and what happens next. For anything else, see contact.