New technique helps robots pack objects into a tight space

MIT researchers are using generative AI models to help robots more efficiently solve complex object manipulation problems, such as packing a box with different objects. Anyone who has ever tried to pack a family-sized amount of luggage into a sedan-sized trunk knows this is a hard problem. For the robot, solving the packing problem involves satisfying many constraints, such as stacking luggage so suitcases don't topple out of the trunk, heavy objects aren't placed on top of lighter ones, and collisions between the robotic arm and the car's bumper are avoided. Some traditional methods tackle this problem sequentially, guessing a partial solution that meets one constraint at a time and then checking to see if any other constraints were violated. With a long sequence of actions to take, and a pile of luggage to pack, this process can be impractically time consuming.