Most work in manufacturing, logistics, and assembly still relies on human hands. Despite decades of investment in automation, tasks involving variation, tight tolerances, or unstructured environments remain resistant to robotics.
Sze Yuan Cheong witnessed this challenge firsthand. After spending more than a decade building and operating four manufacturing businesses, he became convinced that the gap between what robots could do and what factories actually needed was not merely an engineering issue—it was fundamentally misframed. That conviction led him to co-foundDevol Robots, a physical AI startup developing a force-based world model for robotic manipulation.
Now approaching three years since incorporation, Devol Robots employs an 18-person team and is deploying its model with U.S.-based industrial clients.
Born in Singapore and raised in Malaysia, Cheong moved to the United States for college, where he quickly became interested in startups. During a summer trip home, he was pitched a business idea on his second day back. By the third day, he had launched the company. At 21, he chose not to return to complete his degree, instead running the venture full-time.
Over the following years, Cheong built four traditional manufacturing-related businesses. His academic background in industrial and process engineering provided deep insight into how goods are produced—and where production systems fail. While financially successful, he eventually found the work lacking long-term purpose.
That restlessness, combined with a lifelong fascination with technology, led him to reconsider what he wanted to build next.
Years of operating manufacturing companies gave Cheong a clear understanding of the industry's persistent bottlenecks. Assembly lines, logistics handoffs, and tasks involving modest variation remain largely manual despite automation investments. The disconnect between robotics research and factory-floor deployment represented both a problem and an opportunity.
The turning point came when Cheong reconnected with Elijah Yi Herng Ong, a researcher who had completed a master's degree focused on reinforcement learning for robotic grasping and had been admitted to Stanford's PhD program, which he ultimately declined. Ong instead joined a robotics startup in Austin, Texas, where he worked on building a robotic arm from the ground up. There, he encountered impedance control—a method of guiding robots through stiffness and compliance, inspired by how humans regulate muscle tension.
Together, Cheong and Ong developed the mathematical foundations for describing physical embodiment through force-based control and founded Devol Robots to commercialise the research. Their aim: build a foundational model that teaches robots to interact physically with objects—not by watching videos, but by learning from forces, torques, and contact dynamics, much like humans learn through touch.
The prevailing method in robotic learning today is often described as the vision-language-action pipeline. In simplified terms, a robot's movements are tokenised, processed through a large language model, and used to predict future visual states, which are then translated back into motor commands. While functional, this method infers physical interaction primarily from vision.
Source: International Business Times UK
