Industrial
Physical AI promises robots that can perceive, act and adapt in the real world with far less task-specific engineering than traditional automation. But as AI systems move from the digital to the physical world, new requirements emerge: robots ultimately interact with the physical world through grippers, sensors and tools that make direct contact with objects.
Intelligent models and policies remain essential, but they are only a part of the equation. 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.
Models can generate actions, but hardware must execute those actions.
In selecting the right EOAT for physical AI-driven robotic applications, four requirements are critical. First, the ability to accommodate real-world variability: in a real-world manufacturing environment, robots need to handle variation in parts, positioning and operating conditions. 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.
Second, more capable models require a reliable execution layer. 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: models can generate actions, but hardware must execute those actions.
Source: The Robot Report