Kite

What is Kite?

Kite is a robotics IDE and MCP-based workspace for training autonomous robots. It brings simulation, models, agents, and real hardware into one environment so researchers can focus on robot behavior instead of stitching together infrastructure and setup.

The product is built around robot development workflows such as handling URDFs, packages, libraries, simulation compatibility, and training pipelines. It also offers cloud compute and access to frontier models for robotics work, with support for both default workflows and bring-your-own frameworks, robots, and hardware.

Key Features

• Unified robotics workspace — combines simulation, models, agents, and hardware in one place so training workflows do not need to be assembled from separate tools.
• Hardware-aware setup — handles URDFs, packages, libraries, and sim-to-real compatibility upfront, which is intended to reduce configuration work before training starts.
• Model access for robotics training — includes models from Physical Intelligence, World Labs, NVIDIA, and Google, plus Kite’s own robotics coding agent.
• Cloud compute on demand — provides cloud GPUs and CPUs that scale with the training run, removing the need to maintain a local compute rig.
• Supports multiple robotics stacks — works with ROS, MuJoCo, Kimodo, cloud GPUs, and custom stacks, so teams can use existing tools instead of starting over.
• Robot and workflow coverage — positioned for robots such as Unitree, Boston Dynamics, SO-100, and custom URDF-based setups, across tasks like locomotion, manipulation, and humanoid control.

How to Use Kite

A typical workflow starts by opening a project and connecting the robot, simulator, or dataset you want to work with. From there, you can use Kite’s defaults or bring your own frameworks, robot models, and hardware.

Users then configure training in the workspace, run simulation and model-driven experiments, and use cloud compute when the workload needs more resources. The product also shows starter motions and dataset-generation prompts, suggesting a flow that includes describing a desired motion, generating training data, and iterating on robot behavior.

Use Cases

• Training locomotion policies — researchers working on quadrupeds or legged robots can use Kite to run simulation-backed training and iterate on movement behavior.
• Manipulation and arm control — teams building arm or hand behaviors can use the IDE to organize simulation, models, and training runs in one workspace.
• Humanoid robot development — users training humanoid control policies can use the platform to manage robot-specific setup and compute without building all infrastructure locally.
• Synthetic data generation for robot behavior — teams can generate training data for motions or tasks such as walking, sitting, picking up objects, or waving.
• Bring-your-own-stack robot projects — researchers with existing ROS, MuJoCo, or custom URDF-based workflows can use Kite as an integrated layer without abandoning their current tools.

FAQ

What is Kite used for?
Kite is used to train autonomous robots by combining simulation, models, agents, and hardware in one workspace.

Who is Kite for?
The site positions Kite for researchers and robotics teams that want to focus on robot behavior rather than infrastructure and setup.

What kinds of robots does Kite support?
The page mentions Unitree, Boston Dynamics, SO-100, and custom URDF-based robots.

Does Kite require a local compute rig?
No. The product page says Kite provides cloud GPUs and CPUs that scale to the training run.

Can existing tools still be used with Kite?
Yes. The page says users can use Kite’s defaults or bring their own frameworks, robots, and hardware, and it references ROS, MuJoCo, Kimodo, and custom stacks.

Alternatives

• MuJoCo — a physics simulation platform used for robotics work, but not presented here as a full IDE or end-to-end training workspace.
• Isaac Sim — a robotics simulation environment that is often used for robot development and testing, with a stronger focus on simulation than on the integrated training workspace described for Kite.
• ROS-based custom stacks — teams can also build their own robotics pipeline around ROS and related tools, which offers flexibility but requires more setup and integration work.
• General cloud ML platforms — broader compute and model-training platforms can support robotics workflows, but they typically are not specialized around robot hardware, simulation, and sim-to-real setup.