When Robot Safety Becomes a Perception Problem

For decades, robot safety was based on separation. Cages, light curtains, guarded workcells, and emergency stops kept machines and people apart. Physical AI changes that model. Robots increasingly share space with people, work at close range, and move through dynamic environments designed for humans.

At ImagingNext 2026, Mike Nielsen, Chief Marketing Officer at RealSense, presents “Safety Used to Be a Mechanical Problem. In Physical AI, It’s a Perception Problem” The session examines why vision and perception are becoming foundational elements of safe robotic applications.

About Mike Nielsen

Mike Nielsen is Chief Marketing Officer at RealSense, where he leads marketing strategy, brand messaging, and market expansion. His career spans engineering and marketing, with experience translating technical innovation into market-ready solutions. Before joining RealSense, he held leadership roles at Intel and Cisco, working across vision, biometric authentication, security, video, and automation technologies. Nielsen holds a BS in Electrical Engineering from the University of Nevada, Reno.

Why perception matters for robot safety

In physical AI, safety is not defined by the robot alone. It depends on the complete application: how accurately the system senses its surroundings, how quickly it interprets changes, and how reliably it responds in real time.

This places vision and perception at the center of the safety conversation. Cameras are no longer supporting components used only for observation. They provide the spatial information robots need to navigate, interact with people, and respond to changing environments.

Building this perception layer requires several capabilities to work together. Depth accuracy affects how precisely a robot understands the position of nearby people and objects. Close-range performance and field of view determine what the system can detect around the robot. Latency influences how quickly visual information becomes an action, while environmental robustness affects performance under changing light, temperature, or operating conditions.

Edge processing and system redundancy add further layers to the architecture. Together, these capabilities form what Nielsen describes as the visual cortex of physical AI: the perception layer that transforms visual data into the spatial intelligence required for safer robotic operation.

Using the RealSense D555 or newly announced D585 as a technical framework, the session will explore how these capabilities can be applied to advanced safety applications in robotics and related fields. The focus is on the architecture and engineering requirements behind perception, rather than on the camera as an isolated product.

The future of robot safety will not be shaped only by stronger barriers. It will increasingly depend on systems that can see more clearly, interpret their surroundings more quickly, and react more intelligently.

What you’ll take away

• Why physical AI is shifting robot safety from mechanical separation toward real-time vision and perception.
• How depth accuracy, close-range performance, field of view, and latency influence safe robotic applications.
• Why environmental robustness, edge processing, and redundancy must be considered as parts of the complete perception architecture.
• How visual data becomes the spatial intelligence robots need to navigate and interact safely.
• How depth-camera architecture can support advanced safety applications in robotics and beyond.