AI robotic picking is the use of cameras and AI so that a robot can pick up an object it has never seen before. Normally a robot is programmed point by point by a specialist, who tells it exactly where the part will be and how to grip it. That works only if the part is always in the same place.
With AI robotic picking, the robot looks at the object and works out the grip itself. The robot's cameras take pictures of the bin. Software builds a 3D shape of each object, finds the points where the gripper can hold it, and plans a path for the arm that does not hit the bin. After the pick, the cameras check that the object is held properly.
Picking from a jumbled bin is far harder than picking from a conveyor belt. On a belt, the parts lie flat and spaced apart, and a simple camera can find them. In a bin, parts lie on top of each other, hide each other and point in every direction. A shiny metal part also reflects the light and confuses the camera. So the software must decide which part is on top, and whether the arm can reach it without knocking the others.
For example, a bicycle-parts maker in Ludhiana changes its product every few days. A conventional robot would need a specialist to reprogram the grip and the path for every new part. A robot that picks by sight needs only a few pictures or the part's drawing, and it can handle a part that the factory has never made before.
This part depends on machine vision, which is the same camera technology used for inspection, and on a robotics foundation model. The robot arm is described under industrial robots and cobots. The idea belongs to computer vision in general.
The hard part is speed and certainty. A picking robot must pick at a speed close to a person's, and it must not drop a part, because a drop stops the line. Because of these two needs, picking from a jumbled bin is still one of the hardest factory jobs to automate.