Spatiousdata provides dense, per‑frame labeled spatial‑temporal datasets captured from a variety of robot platforms—including humanoid, wheeled, fixed‑base, and egocentric systems—in real‑world environments. The data are pre‑processed into simulation‑ready formats compatible with major frameworks such as ROS, Unity, and Unreal, enabling robotics companies and AI researchers to build high‑fidelity digital twins, train perception models, and pre‑train world models without the cost of collecting and annotating raw sensor streams.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics developers and AI researchers often lack access to large, accurately labeled spatial‑temporal datasets captured from real‑world robot platforms, making it difficult to train perception models or build reliable digital twins for simulation and testing.
Solution
Spatiousdata aggregates dense, labeled object and event data from a variety of robot modalities—including humanoid, wheeled, fixed‑base, and egocentric platforms—operating in diverse real‑world environments. The data are pre‑processed into simulation‑ready formats that can be ingested directly by major simulation pipelines or custom environments. By providing high‑dimensional (0+ DOF) and full‑dimensional (0 D) recordings, the platform enables users to generate high‑fidelity digital twins, train robot vision systems, and pre‑train world models without the overhead of collecting and annotating raw sensor streams.
Target Audience
Primary customers are robotics companies, autonomous‑vehicle developers, and AI research teams that require realistic spatial‑temporal data for simulation, perception model training, and digital‑twin creation.
Features
- Dense, per‑frame labeling of objects and events captured across multiple robot types and environments
- Support for a wide range of degrees of freedom and full‑dimensional data representations
- Pre‑processed datasets compatible with leading simulation frameworks (e.g., ROS, Unity, Unreal)
- Modular data packages allowing selection of specific robot form factors or sensor modalities
- Continuous updates with new real‑world recordings to expand coverage and scenario diversity