Teams typically bring a brand-new piece of hardware fully online with
Cyberwave in about 2 days, versus the months a bespoke integration
usually takes, because the digital twin, transport, UI, workflows, and
data tooling already exist. You only supply the asset and the driver.
Bring your own hardware
Create an asset for your hardware
Every twin starts from an asset, the 3D and kinematic definition of your
hardware (a URDF plus its meshes). Upload yours to create a custom asset,
which you can keep private to yourself, share with your organization,
or make public to everyone on the platform, depending on your needs.Need a reference to model yours on? Download a complete sample URDF (a DJI
Mini 4 Pro drone) showing link, joint, material, and mesh-reference structure:
Create an Asset
Step-by-step: prepare a URDF ZIP and create your asset from the dashboard.
Sample URDF: dji-mini-4-pro.zip
One
base_link, four propeller links, and four continuous joints in a single file.Write a driver for your hardware
A driver is what makes your asset come alive. It’s a small Docker
container that bridges your device’s native interface (ROS, serial, REST,
gRPC, VDA5050, OPC UA, Modbus, or any other protocol) to Cyberwave’s MQTT
layer. It publishes telemetry (joint states, odometry, camera frames, sensor
data) up to the twin and turns dashboard, SDK, and workflow commands into
actions on the device.You don’t have to start from scratch: scaffold a complete, production-ready
driver with the AI driver skill or one of the official SDKs, then
fill in the hardware-specific logic.
Writing Compatible Drivers
The full guide: container contract, the
cw-driver.yml interface manifest,
MQTT catalog, environment variables, and packaging.Everything you unlock
Once your hardware is paired, it gets the full platform, the same capabilities every catalog device enjoys:Workflows
Chain perception, models, and motion into repeatable automations, low-code or in Python.
Replay & recording
Record episodes, scrub the timeline, and export datasets for training and review.
Teleoperation & remote takeover
Drive your hardware from anywhere and take over from an AI policy with one click.
Controllers & models
Assign keyboard, teleop, or trained AI policies as the controller for your twin.
Simulation
Validate behavior against the digital twin before it ever touches real hardware.
Digital twin
A live, synced virtual mirror of your hardware that the whole platform builds on.
Works with your existing stack
Because integration happens at the driver layer, Cyberwave is protocol-agnostic and sits alongside the tools your team already uses. The same driver pattern bridges any of these to the digital twin:- ROS 1 / ROS 2: robot arms, mobile robots, and sensor stacks
- VDA5050: AGV / AMR fleet communication
- OPC UA: industrial automation and PLC connectivity
- Modbus TCP / RTU: sensor and actuator networks
- gRPC / REST: custom services and microservice architectures
- Serial / USB: direct device control (servos, microcontrollers)
Open-source resources
All integration libraries and reference drivers live under the cyberwave-os GitHub organization:| Repository | Description |
|---|---|
| cyberwave-edge-core | Edge orchestrator |
| cyberwave-edge-python | Python Edge SDK |
| cyberwave-python | Python platform SDK |
| cyberwave-edge-camera-driver | Reference camera driver |
| cyberwave-edge-so101 | Reference SO-101 arm driver |
| driver-skill | AI scaffolding skill for new drivers |
Next steps
Create an Asset
Upload your URDF and create a custom asset, public or private.
Writing Compatible Drivers
Build the driver that connects your hardware to its twin.