FCC restrictions on robots could accelerate the shift to homegrown AI

Robots are back in the national spotlight, and this time the conversation isn’t just about what they can do. It’s about who controls them and what data they collect.

Restrictions on foreign-made robots by the Federal Communications Commission (FCC) highlight the growing scrutiny of these machines as connected infrastructure. As robots do more and more work in factories, warehouses and other high-risk environments, questions about how they use data and make decisions become increasingly difficult to ignore.

This scrutiny is likely to accelerate the move to on-premise AI architectures and smaller, specialized models that can get closer to where robots operate. The more companies want to control who can access a robot and its data, the more incentive they have to keep sensitive processes close to the machine.

Today, companies deploying physical AI must decide which AI workloads belong on the robot, what can stay in the cloud, and whether the underlying technology stack can meet growing expectations for supply chain security and transparency.

Robot safety is becoming an architectural issue

In July, the FCC added advanced robotic devices made abroad to a list of technologies it believes pose unacceptable national security risks. This designation prevents new devices from receiving the FCC authorization generally required to be imported, marketed, or sold in the United States

The restrictions apply broadly to advanced mobile robots, including humanoids and quadrupeds, which can gather detailed information about the environments around them. Officials have warned that foreign-made robots could provide foreign actors with a way to conduct surveillance, collect sensitive data or remotely interfere with machines operating inside American facilities.

But the origin of the hardware is only part of the security equation. If a robot continuously collects information about a facility and sends it to an external cloud service for processing, companies must also consider where that data goes and who has access to it.

Editor’s Note: The robot report will host a free webinar on October 27 titled “The FCC Ruling on Robotics: What Automation Buyers Need to Know.” Join leaders from Vecna ​​Robotics, Locus Robotics, and the Association for Advancing Automation (A3) for a discussion about what the FCC’s action means for the robotics industry and organizations investing in automation.

Bringing intelligence closer to the robot

Maintaining multiple inferences on the robot or within a structure was already attractive because it improves latency and reliability. Recent regulatory change could make local processing a higher priority for a wider range of AI workloads, especially those involving internal operational data.

For core control functions such as motion control and security monitoring, local processing is non-negotiable. The biggest change is likely to happen higher up the stack, where robots interpret instructions, analyze their surroundings, respond to anomalies, and make task-level decisions.

Such workloads can be compute-intensive, and the cloud offers more processing power than most edge hardware. But relying on external cloud resources can introduce network dependency and require operational data to leave the facility. Companies will likely need to manage more of that intelligence locally within tighter processing constraints.

Why SLMs could gain ground

The more organizations move higher-level intelligence to devices, the less practical it becomes to rely on cloud-dependent large language models (LLM) for every task. This strengthens the case for Small Language Models (SLMs) designed for industrial environments.

A robot operating in a defined environment does not always need a built model to handle almost all questions. It needs someone who understands the context of the facility and can perform the required tasks reliably.

This doesn’t mean the robot needs to be tied to a single job. With the right context and access to local data, an SLM can support a wider range of applications within the same facility. For example, a robotic arm working on a production line could be moved elsewhere in the factory and adapted to perform a similar task in a different manufacturing environment.

SLMs can be optimized on a factory floor’s data and run directly on edge hardware with lower compute requirements than larger generic models. For many industrial use cases, the combination of specialization and flexibility in changing local implementations can make them better suited to the job.

Greater local control means greater integration work

These changes raise the bar for system design by U.S. companies that build or integrate robots. It is necessary to ensure that controllers, sensors, security systems and AI hardware can work together reliably and that the entire system can be protected and maintained over time.

When you purchase a finished robot, many integration and certification decisions have already been made. Building one from components places a greater engineering burden on your organization: how will the components be integrated? How will security be validated? How will the firmware be maintained and where will the critical parts come from?

The right suppliers can help answer these questions with tested components, traceable sourcing and implementation support.

US companies need vendors who can show how their components were tested together, document the provenance of critical parts, and provide the necessary security and governance support post-implementation. When these elements have already been validated to work together, builders can spend less time troubleshooting integration issues at the component level and more time focusing on the complete system.

Build for control from the start

The most immediate effect of the FCC’s action concerns supply. Its long-term impact could be even bigger: architecture.

As robots increasingly move into sensitive and mission-critical environments, companies will need to evaluate them not just in terms of their capabilities or performance. They need to know whether critical processing can remain local, where the robot’s data goes, and how much trust they can place in the underlying components.

Companies that introduce such controls early on will be in a better position to implement physical AI without creating problems that need to be resolved later.

About the author

Jenny Shern, general manager of NexCOBOT, is responsible for managing the company’s sales and business strategy, robotic and motion control product development, and project implementation. Shern specializes in strategic partnership building and business development in the IoT automation, robotics and retail industries. He previously worked at NEXCOM from 2005 to 2018, where he led sales teams in building a global channel network and key multinational automation accounts. In 2018, NexCOBOT separated from the NEXCOM IoT Automation Solutions Business Group, focusing on providing open robotic and machine control systems for industrial and collaborative robotic applications.