This is an important point. Grasping itself isn't that difficult if you know the object and its orientation. It's a pre-configured action (grip here and here, move, release). Ocado, a large online-only grocery store in the UK have a demo they show trade shows that uses a suction cup. They don't bother with a grasper, they just locate a big enough surface to latch onto and use that. That's considerably easier than picking things up using fingers and you can use fairly simple algorithms to detect planar surfaces.

The problems start when things are tightly packed. One object might be in the way of your gripper so you can't take the thing you want. What happens if you drop the object? Does your robot know how to move things aside to grab the item you want?

And on top of this you have the whole issue of detecting objects in a cluttered environment: this requires state of the art semantic segmentation, robust 3D vision that will work on non-cooperative targets and a classifier that can work with occlusions and huge numbers of objects reliably.

I think it's reasonable that some items can be scanned upon entry into the warehouse, e.g. on a laser triangulation turntable that also grabs a video as it rotates. With this sort of task you want to exploit as many priors as you can find. If you look at papers (and factories), you see a lot of tricks: objects on flat surfaces that make segmentation easier, small numbers of known objects, etc.