Why Physical AI Still Depends on C and C++

We are entering a new era of robotics in which machines are breaking out from behind their factory fences. They work alongside people, adapt to changing environments, and increasingly make decisions on their own. From humanoid robots and collaborative robots to AGVs, Physical AI is moving from controlled settings into the real world.

While much of the attention focuses on artificial intelligence, intelligent behavior ultimately depends on reliable physical execution. A robot can have sophisticated perception and planning capabilities, yet it still needs to move, balance, react, and operate safely in the physical world. That responsibility falls to the software that drives the motors, actuators, and embedded electronics.

Modern robotics architectures can be simplified into three layers: Sense, Think, and Act. The Sense layer processes sensor inputs such as cameras, lidar, and force sensors. The Think layer performs planning, navigation, decision-making, and AI-driven reasoning. Finally, the Act layer determines what physically happens: how a motor responds, how balance is maintained, and whether a safety mechanism activates in time.

Unlike the 1st two layers, this Act layer continues to be dependent on the C and C++ language. The reason is simple: physical systems demand deterministic performance. A humanoid robot may execute motor control cycles every 50 microseconds, leaving a fraction of that time to perform calculations and respond. Missing a deadline directly affects stability and safety. C and C++ provide the performance needed for these demanding control loops while allowing engineers to guarantee predictable timing through interrupts and real-time scheduling mechanisms. In a balancing robot, delays measured in milliseconds can be enough to introduce instability.

Equally importantly, embedded controllers often operate with constrained memory and require direct interaction with hardware. C and C++ give developers precise control over memory usage and enable direct access to processor registers and electronic interfaces. This allows software to communicate efficiently with low-level sensors, actuators, and motor drivers while maintaining the deterministic behavior that real-time systems require. AI may decide where a robot should move, but low-level software determines how it moves and whether it does so safely.

This is where the themes begin to converge from our previous blogs in our series on robotics. In our first blog, we explored the often-overlooked role of compilers and libraries, and why the software toolchain itself forms part of the functional safety chain. In our second blog, we examined the Update Paradox of Physical AI: autonomous systems must continuously evolve, yet every update can potentially affect previously established qualification evidence.

Both blogs discuss challenges that become particularly significant when looking at the Act layer. The software for this layer is predominantly written in C and C++, the languages that ultimately translate decisions into physical actions. This makes trusted toolchains and Continuous Qualification essential foundations for safe, reliable, and trustworthy autonomous systems when scaling from prototype to deployment.

Want to learn more? Join our CPO Sjoerd van der Zwaan on 24 June for our webinar
Fixing the Foundation First: Continuous Confidence in Robotics Software Infrastructure.

Register here