Physical AI Matters More Than Humanoid Robots

Writing about last year’s Hannover Messe, I made a point of calling out the small number of humanoid robots I saw at this huge industrial trade show. Fast-forward to 2026, and I’m about to jump on a plane to Germany for this year’s event. I know that I’m going to see a lot of humanoid robots. I know that most of them will be Chinese, a few will be European, and many of them will be impressive. And as Charlie Dai and I argue in our new report, I am confident that exhibitors’ and attendees’ apparent obsession with legs and arms misses the real story.

Our new report, Physical AI Perceives, Reasons, And Acts In The Real World, argues that the more important story is really the growing capability of physical AI. Humanoid robots like those that my colleague Charlie Dai recently wrote about benefit from that, for sure, but so do many other types of physical automation — and most of them are cheaper, more durable, and more useful than the bundle of compromises required to squeeze batteries, computers, sensors, actuators, and more into a vaguely humanoid shape.

Physical AI touches the real world

Physical AI is all about bringing AI into the real world, making AI aware of what’s happening around it, and giving AI the ability to affect — touch — that world. As we describe in the report, physical AI comprises four broad capabilities. Each is a huge field of fast-moving research in its own right, but something special happens when all four are brought together to deliver physical AI, which:

• Models and simulates the real world. Modern world models are neural networks trained on lots of data. Unlike an LLM, which generates text, these world models create simulated physical spaces and generate interactions within them. While a world model might not fully understand Newton’s law of universal gravitation or the quadratic drag equations used to account for friction, it has observed their effects. It can replicate them to simulate a falling object.
• Perceives the real world. Physical AI systems are enriched by a wide range of relevant sensor inputs from the real world, including sound, light, temperature, tactile feedback, and more.
• Reasons about the real world. In the context of physical AI, conceptual models and sensor-based observations are means to an end: They provide a physical AI system with the facts it needs to reason about how best to achieve its objective.
• Acts upon the real world. Conceptual models, sensor-based observation, and AI reasoning combine with physical systems like a robot’s hands or an autonomous vehicle’s steering controls to effect change in the real world: The robot picks up a banana without squeezing too hard, and the autonomous vehicle steers around an obstacle in its path. These abilities may be instantiated in a physical machine (such as a robot or autonomous vehicle), in which case this capability is often referred to as embodied AI.

I’ll be looking for evidence of physical AI throughout my week in Hannover, and I’ll blog about that (and other highlights of the show) once I’ve had time to digest everything I see.

The original article is here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/Ilya Lukichev