Physical AI and the Forgotten Lesson of Object Permanence

How physical AI–driven robots are learning to see, remember, and reason about the real world — just like we did as babies.. 

When I first started following artificial intelligence, I was fascinated by chatbots that could write poetry and answer trivia questions in seconds. But as I dug deeper, I realized the real magic — and the real challenge — begins when AI steps off the screen and into the physical world. Today, robots, drones, and self-driving cars are no longer sci-fi props; they’re real machines trying to make sense of our messy, unpredictable environment. And in this world, an old childhood lesson — object permanence — suddenly becomes one of the biggest hurdles.

What is Physical AI?

Physical AI refers to artificial intelligence that operates in the physical world, sensing and interacting with tangible objects and environments. Think of a robot that picks items off a shelf, a self-driving car navigating traffic, or an industrial arm sorting packages in a warehouse. Unlike purely digital AI, Physical AI must perceive, understand, reason about, and manipulate real-world elements with all their unpredictable quirks.

This branch of AI combines computer vision, robotics, sensor fusion, reasoning, and real-time decision-making. It’s not just about answering questions or generating images — it’s about perceiving a messy environment, making sense of it, and taking action in ways that are safe and reliable.

Object Permanence: A Core Lesson for Building Robots

Object permanence is a foundational concept in human cognitive development, first described by Swiss psychologist Jean Piaget. It refers to the understanding that objects continue to exist even when they are out of sight. A baby playing peekaboo learns that when you hide a toy under a blanket, the toy doesn’t cease to exist — it’s just hidden.

For humans, this realization comes naturally by around 8 to 12 months of age. For robots, however, this simple-seeming idea requires sophisticated reasoning about the physical world.

When engineers build a robot, they’re not just assembling motors and sensors — they’re programming it to reason about what it sees (or doesn’t see). If a robot can’t infer that an obscured object still exists, it might make dangerous or costly errors.

Why Object Permanence and Reasoning Matter

Imagine a warehouse robot tasked with moving boxes. If a box rolls behind another or is temporarily hidden by a worker, will the robot “know” the box still exists and plan accordingly? If an autonomous car loses sight of a pedestrian who steps behind a parked vehicle, does its system reason that the pedestrian is likely to re-emerge — or does it assume they disappeared?

Failing to model object permanence and the reasoning behind it can lead to dropped items, damaged goods, or even accidents. Building robust perception and logical inference into Physical AI is critical for safe, practical robotics.

The Challenge: Bringing Together Sensing, Memory, and Reasoning

In purely digital AI, information is abstract — words or data points. In Physical AI, the system must perceive the environment through noisy sensors (cameras, lidars, radars) and integrate that input into a coherent, persistent mental model. This demands both memory and reasoning: a dynamic understanding of what’s where, what might happen next, and what remains true even when out of sight.

Techniques like scene representation networks, predictive modeling, and simultaneous localization and mapping (SLAM) are advancing robots’ ability to reason about hidden objects and anticipate how things might move. It’s a big step toward building machines that can safely and intelligently share our world.

A Humbling Reminder for Robot Builders

Ironically, as engineers design ever more advanced robots, they are revisiting a lesson every toddler learns: the world doesn’t vanish when you close your eyes.

For anyone building a robot — whether it’s a helper at home, a delivery bot on the sidewalk, or a drone inspecting a wind turbine — the ability to reason about hidden objects is fundamental. Without it, even the smartest robot will fall short in the unpredictable, cluttered real world.