RobotData provides a data stack for physical AI that helps roboticists and robotics companies collect, evaluate, and leverage high‑quality training data for their robots. Its platform lets users identify required data types, debug robot behavior by tracing it back to specific datasets, and enable continual learning from in‑field robot data, improving performance and safety.
Funding
Funding not disclosed
Founders
Product
Problem
The data required to train advanced robots—covering type, quality, and scale—is currently unavailable, leading to inefficient collection efforts and limited insight into how data impacts robot performance.
Solution
RobotData offers a data stack tailored for physical AI that helps roboticists determine the exact data needed for training, trace robot behavior back to its originating training inputs, and apply in‑field data for continuous learning. The platform provides tools to assess data quality, quantify its effect on robot outcomes, and streamline the development of safe, high‑performing autonomous systems. By integrating data collection, analysis, and feedback loops, RobotData enables systematic improvement of robot capabilities throughout deployment.
Target Audience
Primary customers are roboticists and robotics companies developing autonomous systems that require systematic data collection, quality assessment, and continual learning capabilities.
Features
- Diagnostic engine that recommends specific data types and quantities required for a given robot task
- Traceability layer linking observed robot behavior to the underlying training datasets for root‑cause debugging
- In‑field data ingestion pipeline supporting continual learning and model updates without manual re‑training
- Data quality metrics and impact analysis dashboards that correlate dataset attributes with performance metrics
- Scalable storage and processing infrastructure optimized for large‑volume sensor and interaction data from physical robots