The Embodied AI

Humans navigate a world governed by physical laws, making constant predictions about how objects will move or interact. Yet, we rarely pause to calculate the complex physics behind these predictions. This ability, known as intuitive physics, is a cognitive process that allows us to predict and adjust to our environment seamlessly. But what’s truly fascinating about our intuitive physics is not its accuracy, but rather its flexibility.

Unlike the exact models of physics taught in classrooms, the brain’s internal models are approximate. These mental simulations don’t aim to be perfectly precise—they are built to be good enough. This may sound like a flaw, but there are distinct advantages to these inexact models. The brain balances cognitive effort with the need for speed and adaptability, ensuring that we can make quick decisions in the real world without becoming bogged down by unnecessary complexity.

For instance, when you anticipate where a ball will land, your brain doesn’t compute the precise physics of its trajectory. Instead, it uses a simplified simulation based on past experience and learned patterns of movement. These quick, efficient simulations allow us to act in real-time—whether catching the ball or dodging an obstacle—even if the internal model isn’t a perfect representation of the actual physics at play. The advantage here is clear: speed and adaptability outweigh precision in most real-world scenarios.

Despite this mismatch between our internal models and the exact physics of the world, we consistently manage to perform complex tasks. Our brains can compensate for imperfections in the model by relying on a combination of perception, inference, and real-time feedback. This ability to adjust our actions as new information comes in is what makes intuitive physics so powerful. It allows us to operate in environments filled with uncertainty and complexity, guiding our decisions and behaviors in ways that highly accurate but slower models would struggle to achieve.

Embodied AI with Intuitive Physics

This adaptability offers a fascinating approach for robotics and AI. A robotic system inspired by intuitive physics doesn’t focus on exact physical calculations. Instead, it relies on flexibility, using sensory input to guide its interactions with the world, constantly adjusting as it gathers new information.

Much like the human brain, the robot depends on sensory input to understand its environment. Visual, tactile, and auditory signals help it recognize object properties, distances, and movements. Rather than solving precise equations, the robot creates an approximate internal model of the scene. This model isn’t meant to be perfect but offers a “good enough” prediction of how objects might behave, based on past experiences and simple rules.

As the robot interacts with its surroundings, it uses real-time feedback to adjust its actions. For example, if it tries to pick up an object and encounters resistance or sees the object shift, it immediately changes its grip or angle, just like a person noticing something doesn’t feel quite right. The robot’s actions are always informed by this continuous feedback.

The robot also uses its internal model to plan ahead, simulating different future scenarios. Before taking action, it predicts possible outcomes and chooses the one most likely to succeed. This process doesn’t require vast amounts of computing power—it’s about finding practical solutions rather than perfect ones.

The robot shows flexibility in its decision-making. When it senses new information that doesn’t match its original plan, it quickly updates its internal model and adjusts its actions. This mirrors how the human brain reacts in real time to unexpected changes in the environment.

This framework doesn’t aim for perfection in predicting physical events but prioritizes speed and adaptability. It allows robots to function effectively in unpredictable, real-world situations, which is especially important in the field of embodied AI—where systems need to interact with the world in real time. By adopting the brain’s principle of “good enough” intuitive physics, AI systems learn to adapt, infer, and plan much like humans do in their everyday experiences.

The power of intuitive physics lies in its imperfection. It allows for quick decisions and adjustments, even if the predictions aren’t perfect. This concept offers a promising direction for AI and robotics, enabling systems to navigate complex and uncertain environments without needing a flawless understanding of physical laws.