Kite is a robotics IDE and managed compute platform that unifies simulation, data generation, training, and deployment for autonomous robots.
Funding
Funding not disclosed
Founders
Product
Problem
Developing autonomous robot behaviors requires coordinating multiple fragmented tools for simulation, data generation, training, and hardware integration, which leads to lengthy setup times and complex infrastructure management.
Solution
Kite offers an integrated robotics IDE and managed compute platform that consolidates simulation, training, and deployment workflows into a single environment. The platform automatically handles URDF parsing, package management, and sim-to-real compatibility, allowing robots to be ready for training from day one. On-demand cloud GPUs and CPUs provide scalable compute without the need for local rigs. Built-in agents assist with code generation, debugging, and reward design, while integrated tools like World Labs, SAM 3D, and dataset augmentation streamline synthetic data creation. Users can bring their own frameworks or leverage preconfigured stacks such as ROS, MuJoCo, and Kite’s Kimodo engine, enabling rapid iteration on policies across a variety of robot platforms.
Target Audience
Primary customers are robotics research labs, autonomous‑robot development teams, and small to medium enterprises that need a unified environment to prototype, train, and deploy robot policies efficiently.
Features
- Automatic URDF ingestion and validation with drag‑and‑drop support for custom robots
- Pre‑loaded robot templates (e.g., Unitree G1, Boston Dynamics Spot, SO‑100 arm) with verified collision meshes and controllers
- Integrated simulation backends: MuJoCo for physics and Kimodo for kinematics and motion retargeting
- Cloud compute provisioning (GPU/CPU) that scales with training runs and eliminates local hardware maintenance
- Robotics‑aware coding agent that generates policy scaffolding, suggests fixes, and refactors code across multiple files
- World Labs: text‑to‑3D scene generation with deterministic seeds for reproducible environments
- SAM 3D pipeline: image‑based object segmentation to create physics‑ready 3D assets with material and inertia properties
- Dataset Augmentation Studio: transforms existing HuggingFace LeRobot datasets using vision‑language models while preserving motion trajectories
- Support for frontier Vision‑Language‑Action models (Physical Intelligence π‑series) with automated tokenization, adapters, and training orchestration