bin picking
Bin picking is the job of reaching into a box or tray full of objects dumped in randomly — all jumbled, overlapping, and facing every direction — and pulling one out cleanly. Picture a parts bin at a factory where hundreds of identical bolts lie in a tangled heap. A person does this without thinking: you glance in, see a bolt you can reach, grab it, and lift. For a robot, every part of that is hard, because nothing is where it expects, and the next item to grab is often buried under others.
What makes bin picking so much harder than picking from a neat conveyor is the mess, called clutter. The robot first has to look into the bin with a camera or depth sensor and figure out, from the pile, where individual objects are and which way each is turned — its pose. Then it has to choose one object that is actually graspable (not pinned down by its neighbors), plan a grip that will not collide with the bin walls or knock the pile over, reach in, and lift it out. After each pick the pile shifts, so the robot must look again and start fresh every single time.
Bin picking is one of robotics' classic proving grounds because it combines seeing, planning, and grabbing in a messy, ever-changing scene. It shows up everywhere goods are stored loose: factories feeding parts into machines, warehouses fulfilling orders, and recycling lines sorting waste. Reliable bin picking has been a goal for decades, and modern systems lean heavily on cameras plus learned models to handle the endless variety of piles.
A robot arm above a crate of tossed-in plastic fittings takes a depth photo, spots one fitting whose top is clear, drops a two-finger gripper around it, lifts, and places it on a conveyor — then photographs the crate again before the next pick.
Look, pick one, look again — clutter forces the robot to re-plan after every grab.
Picking from a bin where everything is loose and tangled is called random or unstructured bin picking; the easier case, where parts are neatly arranged in known positions, is structured picking.