Nvidia invests in physical AI to enhance safety for robotaxis and humanoid robots

On the software side, the Halos operating system continuously monitors every hardware block and software library to quickly detect failures, Goel said. It also separates safety‑critical computing tasks to prevent interference.

The system also features the Nvidia Holoscan Sensor Bridge, which links sensor data to safety‑focused processing to readily spot corrupted data. The bridge can be embedded directly into hardware components such as microcontrollers or FPGAs.

Finally, the Halos suite offers Nvidia’s simulation tools for virtual robot testing and an inspection‑lab program that gives robotics firms and partners rapid feedback on any safety issues encountered during development.

Adapting Halos from self‑driving cars to robotics meant reconciling many varying safety definitions. While functional safety standards for autonomous driving are largely consistent across automakers and regions, a hallway‑cleaning robot vacuum faces far different risks than a warehouse forklift moving heavy loads, Goel noted.

Goel told Ars, “We had to reimagine the platform and do substantial foundational work to create a programmable system that lets developers define custom safety functions via exposed hooks, without compromising the core infrastructure we built. Adding flexibility can sometimes reduce control over the stack.”

Custom safety functions are essential because robots often operate in complex, varied environments.

Gold said, “When a robot moves down an aisle at six miles per hour, its sensors cannot detect blind spots or what’s coming around the corner. Consequently, it tends to stop just before a turn, since it lacks awareness of what’s happening in factories, warehouses or hospitals—settings that are unstructured and where hazards can appear from any direction.”

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