How better grippers can unlock physical AI

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Models can generate actions, but hardware must execute those actions. | Credit: OnRoot

Physical AI promises robots that can perceive, act and adapt in the real world with far less task-specific engineering than traditional automation. It’s a compelling proposition: robots powered by AI models trained on vast amounts of data, capable of improving over time and operating effectively in far less structured environments. But how do we get there?

As AI systems move from the digital to the physical world, new requirements emerge. Intelligent models and policies remain essential, but they are only a part of the equation. Robots ultimately interact with the physical world through grippers, sensors and tools that make direct contact with objects.

For physical AI to deliver on its promise, it needs a reliable physical interaction layer: End-of-arm tooling (EOAT) that combines adaptability, sensing and feedback so robots can respond effectively to uncertainty and variation.

In selecting the right EOAT for physical AI-driven robotic applications, these four requirements are critical.

In a real-world manufacturing environment, robots need to handle variation in parts, positioning and operating conditions. Physical AI promises to handle more of this variability with less effort. As advances in multimodal foundation models, world models, robot learning, simulation and other areas make robots increasingly capable, the execution layer grows in importance.

The handling is key here. If the EOAT cannot reliably handle variations in part sizes, shapes, and materials, then the model’s intelligence has limited practical value.

Grippers with adjustable gripping parameters and the flexibility to accommodate different parts and conditions give the system greater freedom to put that intelligence into practice.

Models can generate actions, but hardware must execute those actions. Models can infer that an object should be picked up, but a physical gripper must make contact, apply the right force, detect whether the object is secure, and respond if something changes. Every single time.

Robot motion is relatively mature compared with real-world manipulation. That’s because manipulation depends on physical variables that cannot be eliminated and cannot be modeled perfectly.

This makes basic execution feedback critical. Grip and part detection can confirm whether an object is present and whether a grasp has been successfully completed, giving the system a direct signal that the intended action actually occurred.