Auwomo offers a cloud‑native platform that generates photorealistic multi‑modal sensor data (camera, LiDAR, radar) using a generative world model and automatically annotates it with 3D bounding boxes, segmentation and motion labels. The synthetic datasets are accessible via REST and Python SDKs for seamless integration into autonomous vehicle and humanoid robot perception training pipelines, reducing reliance on costly real‑world data collection.
Funding
Funding not disclosed
Founders
Product
Problem
Autonomous vehicle and humanoid robot developers struggle to obtain enough high‑fidelity sensor data for rare or safety‑critical driving scenarios, and manual labeling of raw sensor streams is time‑consuming and expensive. This limits the speed at which perception and control models can be trained and validated.
Solution
Auwomo provides a data‑driven generative world model that synthesizes photorealistic sensor outputs—including LiDAR point clouds, radar returns, and camera imagery—under physically accurate lighting and material conditions. An integrated data‑loop system automatically annotates the simulated streams with ground‑truth labels for objects, semantics, and motion, delivering ready‑to‑train datasets. The platform runs on scalable cloud infrastructure and exposes APIs that let AI teams plug the synthetic data directly into their training pipelines, shortening the development cycle and reducing dependence on costly real‑world data collection. By closing the perception‑to‑control loop, the solution supports both autonomous driving stacks and humanoid robot navigation.
Target Audience
Primary customers are automotive OEMs, Tier‑1 suppliers, and robotics companies that develop perception and control systems for autonomous vehicles or humanoid robots, as well as research institutions focused on sim‑to‑real learning.
Features
- Generative world model that renders multi‑modal sensor data (camera, LiDAR, radar) with physically based lighting, weather, and texture fidelity
- Automated labeling pipeline that produces pixel‑wise segmentation, 3D bounding boxes, and motion vectors for every frame
- Domain randomization and scenario generation tools to create long‑tail edge cases such as rare weather, sensor occlusions, and complex traffic interactions
- High‑performance cloud‑native simulation engine with GPU‑accelerated rendering and batch processing for large‑scale dataset generation
- RESTful and Python SDK APIs for seamless integration with existing ML training workflows and CI/CD pipelines
- Built‑in sim‑to‑real transfer metrics and calibration utilities to evaluate and minimize the reality gap
- Role‑based access control and audit logging to meet automotive safety and data‑security standards