FieldAI develops Field Foundation Models (FFMs) to provide risk-aware, embodied AI for robotic systems. This technology enables diverse robotic embodiments to operate autonomously in dynamic, real-world environments without relying on GPS or pre-defined maps. The platform scales intelligence across various sectors, including construction, industrial, and federal operations, enhancing efficiency and consistency.
Funding
Funding not disclosed
Founders
Product
Problem
Deploying robots in unstructured, hazardous environments is challenging because they often require GPS, pre-programmed routes, or predefined maps to navigate and perform tasks. This reliance on structured data limits their autonomy and adaptability in dynamic, unpredictable real-world conditions.
Solution
Field AI develops Field Foundation Models™ (FFMs) that enable autonomous operation of various robotic platforms in unstructured environments. These FFMs allow robots to navigate and perform complex tasks without relying on GPS or pre-programmed routes. The technology is compatible with legged, wheeled, flying, and tracked vehicles, and it is resilient to sensor failures. Field AI's solution connects wirelessly and enables autonomous multi-robot operations, making existing robots field-ready.
Target Audience
The primary customers are businesses across diverse industries such as construction, agriculture, oil & gas, safety & security, manufacturing, mining, and power & utility, looking to enhance the efficiency, affordability, safety, precision, and consistency of their operations through robotic automation.
Features
- Field Foundation Models™ (FFMs) for on-edge robotic autonomy
- Risk-aware models for robots operating in dynamic environments
- Compatibility with various robotic sensors and vehicle types (legged, wheeled, flying, tracked)
- Wireless connectivity for autonomous multi-robot operations
- API for autonomy pillars, enabling customers to activate autonomous operations and create custom missions
- Field-tested with tens of thousands of kilometers of fully autonomous traversal