Train in sim.Deploy to the real world.

Quadrotor drone carrying a lidar sensor, with the AyroX logo on its shell, in flight
Real hardware, not a render
RoboLab / by AyroX LabsLive

Your robotics lab
for the AI era.

Build robots. Test ideas. Learn by solving. Bring a problem worth exploring and get closer to a working answer, with AI-assisted builders and simulation in your browser.

Meet RoboLab

Inside the platform

Give your idea a robot. And a world.

Describe a robot in the URDF Builder or shape an environment in the World Builder. Use AI assistance to create a starting point, then inspect and refine the result.

Explore the workspace
RoboLab URDF Builder with an AI-assisted robot design and its 3D preview
URDF Builder · Actual platform capture

AI-assisted robot and world builders

Cloud workspaces with real robotics tools

Hands-on labs and graded feedback

●How It Works

From first training run to production robot

01

Simulate

Build your environment, define your task, and train your policy entirely in simulation. No hardware required to get started.

02

Evaluate

Run your trained policy against structured benchmarks in our cloud-hosted robot fleet. Real hardware, real physics — no lab needed.

03

Validate

Catch the sim-to-real gap before it costs you. AyroX surfaces the delta between simulated and physical behavior, so you know exactly what to fix.

04

Deploy

Push the same policy directly to physical hardware. One codebase, one pipeline, from first training run to production robot.

01 Simulate 04 Deploy

Fly the inventory run in sim first

Our quadrotor flies a warehouse aisle with no GPS. Its lidar maps the racks and flags each box it has scanned. Once the run holds up in simulation, the same policy flies the same airframe for real.

sim · warehouse_inspection.usdsimulating
Simulated warehouse aisle: the AyroX drone flies an inspection pass while its lidar maps the racks and flags scanned stock
task
cycle count
gnss
denied
/scan
3,647 pts
/odom
alt 2.60 m
●In Simulation

A household task, trained end to end

Fetching a coffee is four subtasks and three machines that have to agree on where the cup is. We build the whole sequence in simulation first — navigation, grasp, handoff, placement — and run it until it holds together every time.

sim · coffee_delivery.usdrecorded
task
coffee_delivery
agents
base + 2 arms
horizon
4 subtasks
playback
2.5× real time
Recorded in simulation and played back at 2.5× real time.
  1. 01

    Drive to the coffee station

    The mobile base leaves the hallway and positions itself at the kitchen counter, close enough for the arm to reach across it.

  2. 02

    Grasp the cup, load the shelf

    The counter arm picks the cup off the worktop and sets it down on the base's shelf — the handoff both robots have to agree on.

  3. 03

    Carry it through the house

    The base navigates from the kitchen to the dining room without spilling what it is balancing.

  4. 04

    Place it on the table

    The second arm lifts the cup back off the shelf and places it on the dining table. The task only counts if the cup ends up upright.

One lab. Platforms with their own names.

AyroX Labs builds the robots. The software ships as separate platforms, each named for what it does — and each new one will be too.

●The Cloud Fleet

Real hardware, no lab required

Your policy runs against physical robots you never have to buy, maintain, or stand next to. Queue an evaluation on a platform, then read the sim-to-real delta against the same benchmark you ran in Gazebo.

TurtleBot 3

TurtleBot 3

Waffle Pi

The reference ROS 2 learning platform. Small, predictable, and well documented — the fastest way to get a navigation or SLAM stack running against real wheels.

Drive
Differential
Top speed
0.26 m/s
Rotation
1.82 rad/s
Payload
30 kg
Footprint
281 × 306 mm
Mass
1.8 kg
Runtime
~2 hrs
Lidar
LDS-02
Compute
Raspberry Pi 4
Actuators
XM430-W210

// SLAM, navigation tuning, multi-robot formation

TurtleBot 4

TurtleBot 4

Standard

Built on the iRobot Create 3 base with a depth camera and onboard docking. Handles longer autonomy runs and perception work that the TurtleBot 3 cannot carry.

Base
iRobot Create 3
Top speed
0.31 m/s safe
Unrestricted
0.46 m/s
Payload
9 kg
Footprint
342 × 339 mm
Mass
3.95 kg
Runtime
2.5–4 hrs
Lidar
RPLIDAR A1M8
Camera
OAK-D-Pro
Compute
Raspberry Pi 4B

// Depth perception, autonomous docking, long-horizon runs

OpenMANIPULATOR-X

OpenMANIPULATOR-X

RM-X52-TNM

A 4-DOF arm with a parallel gripper, driven by the same DYNAMIXEL bus as the bases. Pairs with a TurtleBot for mobile manipulation, or runs bench-mounted for pick-and-place.

Freedom
4 DOF + gripper
Reach
380 mm
Payload
500 g
Repeatability
< 0.2 mm
Gripper stroke
20–75 mm
Joint speed
46 rpm
Mass
0.70 kg
Actuators
XM430-W350
Bus
TTL multidrop
Supply
12 V

// Grasping, pick-and-place, mobile manipulation

Built by AyroXWaypoint, AyroX's quadrotor carrying a 360° lidar

Waypoint

AX-WP450L

Our own quadrotor, built around a 360° lidar for indoor mapping. It is where a SLAM and navigation stack written in simulation meets real flight.

Airframe
Quadrotor, X
Frame class
~450 mm
Motors
A2212/10T 1400KV
Mapping
360° lidar
Shell
3D-printed
Power
XT60 pack

// Indoor mapping, SLAM, waypoint navigation

●Research

Measuring the sim-to-real gap

Research

Sim-to-real benchmarks

Structured tasks run in both simulation and on physical robots, so the delta is measured, not guessed.

Research

Policy evaluation

Reproducible evaluation runs on real hardware, with the same protocol every time you change a policy.

Research

Closing the gap

Where simulated behavior diverges from physical behavior, and what actually makes a policy transfer.

Start with RoboLab.

RoboLab is AyroX's developer environment — a browser-based platform where developers learn to write real ROS2 code, run it against a live Gazebo simulation, and see results in RViz2, all in a single tab. No local setup. No Docker installs. No dependency management. Just open a browser and start building.

Try RoboLab
●FAQ

Common questions

What exactly is AyroX?+
AyroX is an infrastructure layer for embodied AI. You train a policy in simulation, evaluate it on a managed fleet of real robots in the cloud, see the sim-to-real delta, and deploy the same policy to physical hardware — one pipeline, end to end.
Do I need my own robots to use it?+
No. That is the point of the cloud fleet. You run your policy on real hardware without owning, maintaining, or physically accessing a single robot. You only need your own hardware at the final deployment step, if and when you want it.
What is the sim-to-real gap, and how does AyroX help?+
It is the difference between how a policy behaves in simulation and how it behaves on a physical robot. AyroX runs the same structured benchmark in both places and surfaces the delta, so you find out what breaks before it costs you.
Is the platform tied to a specific robot?+
No. The simulation environment is hardware-agnostic — your policy does not know or care which physical robot it will eventually run on. You do not rebuild the pipeline for every new platform.
Do I have to re-implement my policy to deploy it?+
No. The same policy you trained and validated deploys directly to your physical hardware. No porting, no re-implementation, no surprise behavior changes at the last step.
How is this different from a simulator or a research framework?+
Most tooling is optimized for either academic research or scaled production. AyroX is built for the middle ground where policies actually get validated and shipped: rigorous enough for real deployment, accessible enough for a two-person team.
Where does RoboLab fit in?+
RoboLab is AyroX's developer environment — a browser-based platform for writing real ROS2 code against a live Gazebo simulation, with results in RViz2. It is the fastest way to start building with no local setup at all.
How do I get access?+
RoboLab is open to everyone. Sign up at app.ayrox.tech/signup with email, Google, or GitHub and start building straight away. Already have an account? Log in at app.ayrox.tech/login.
●Contact

Tell us what you're building.

Whether you want RoboLab access, a cloud fleet evaluation, or a conversation about deploying to your own hardware — send the details and we will route it to the engineer who owns that area.

Send a message

We reply within two business days. No newsletter, no sales sequence.