Symbio Robotics

About Symbio Robotics

Symbio Robotics, founded in 2014 by CEO Max Reynolds, is positioned to address complexities in industrial manufacturing through artificial intelligence and robotics. Reynolds, who holds a B.S. in Mechanical Engineering from the University of Southern California, developed a passion for advanced manufacturing and robotics at a young age and later pursued a PhD at UC Berkeley before co-founding the company. His background includes hands-on experience on an F-18 production line and involvement in the Formula SAE Design team.

The company's core business revolves around its software platform, SymbioDCS, designed to enhance existing industrial robots. This platform acts as an industrial robotics middleware and Python programming framework, enabling robots to become more intelligent and flexible. By integrating real-time data from sensors and cameras, Symbio's software allows robots to learn from and adapt to their tasks, a significant shift from traditional, rigid automation. This technology is particularly focused on solving challenges in final assembly, a frontier that has been difficult to automate. The business model appears to be centered on licensing its software to major manufacturers and working with automation integrators. Symbio has secured partnerships with prominent automotive companies, including Ford, Toyota, and Nissan, to implement its technology on their production lines.

The primary product, SymbioDCS, is robot-agnostic, meaning it can be retrofitted onto a variety of existing industrial robots, regardless of the manufacturer. Its key feature is the use of AI to enable human-machine collaboration, allowing programmers and factory floor domain experts to more easily create and adapt automation solutions. For clients like Ford, this has resulted in tangible improvements, such as a 15% reduction in cycle time and a 50% improvement in changeover time for new products in transmission assembly. The system runs on edge computing infrastructure, processing information locally to instruct factory systems, which allows for the automation of complex tasks that previously required manual intervention. This approach has been applied to assemble intricate components like torque converters and to perform tasks on moving production lines without stopping.