Better grippers unlock physical AI's real-world promise

The article positions end-of-arm tooling as a linchpin for physical AI, which promises robots that can adapt with less task-specific engineering. It argues that while models, simulation, and vision are essential, they are insufficient without a reliable execution layer. For instance, a grasp that works in simulation may fail in practice due to real-world variability, highlighting the need for physical feedback like force and proximity sensing.

OnRobot's role as a provider of such tools, exemplified by the RG2-FT gripper, suggests a commercial stake in this narrative. The article emphasizes that general-purpose robots still need a portfolio of end-effectors—2-finger, 3-finger, vacuum, and magnetic tools—to handle diverse tasks, implying that hardware flexibility is as important as software flexibility.

The piece is authored by OnRobot's Director of Global Business Development, so it blends technical argument with a promotional angle. Still, it underscores a growing consensus that physical AI's next phase will depend on both stronger models and more capable hardware, making end-of-arm tools integral to learning systems rather than afterthoughts.