Gravis Robotics has raised $200 million during its Series A funding round backed by SoftBank. The funding will accelerate Gravis’s mission in physical AI — scaling autonomous heavy machinery across job sites globally.
Global infrastructure is in the middle of a massive expansion. Scaling the energy networks and data centres required for the AI economy is driving unprecedented demand, and the need for housing, transit, and climate-resilient infrastructure is equally urgent. Across every sector, construction has become the primary bottleneck, and we cannot reshape the physical world fast enough to keep up with demand. That shortfall stems from a severe labour crisis, but, according to Gravis Robotics, fixing it isn’t just about hiring more people — it requires augmenting the workforce through automation.
How Gravis Robotics’ autonomous equipment is specialized for construction
Most physical AI operates in static worlds. Self-driving cars steer around obstacles on smooth pavement, while humanoids and robotic arms move objects across fixed countertops or warehouse floors — leaving their surroundings completely undisturbed. But heavy equipment operates differently. It intentionally reshapes the environment.
Gravis Robotics intends to solve this challenge with models enriched by vast simulated experiences and that efficiently bridge the sim-to-real gap.
This synthetic training lets Gravis Robotics move billions of cubic yards of virtual earth and enables its software to bring factory-floor precision to historically unpredictable civil job sites. Rather than simply imitating individual operators, its AI world model incorporates the distinct performance characteristics of a wide range of manufacturers, making it generalizable and adaptable.
Dominic Jud, CTO and co-founder of Gravis Robotics, says that “Skilled operators read the earth through subtle physical feedback — listening to the engine strain, sensing the machine vibration, and reacting to hydraulic resistance. Our AI takes that same physical input and grounds it in machine telemetry, responding to varying subterranean forces and soil mechanics at microsecond speeds. We didn’t try to simplify the world for our software; we gave it the physical intuition to handle real job sites with precision that goes beyond what any human can feel from inside the cab.”
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