AI System Uses Teamwork to Efficiently Pack Objects Like a Pro

MIT researchers used a form of generative AI called a diffusion model to efficiently solve the problem of packing objects into a tight space. Their method generates solutions that satisfy all constraints at once.

The technique trains individual models for each type of constraint (like avoiding collisions). The models then work together to find solutions that meet all constraints.

The researchers generated training data by using algorithms to create thousands of tight packings that met constraints like stability. This allowed quick generation of solvable scenarios.

The method outperformed others in experiments, producing more effective packing solutions that were stable and collision-free. It was also able to generalize to novel scenarios.

In the future, the researchers want to test the technique on robots that move around rooms and enable it to tackle new problems without retraining on new data.