Science, Space and Technology News 2023
Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.
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. Credit: Courtesy of the researchersresearchers used a form of generative AI, called a diffusion model, to solve this problem more efficiently. Their method uses a collection of machine-learning models, each of which is trained to represent one specific type of constraint.
This figure shows examples of 2D triangle packing. These are collision-free configurations. Credit: Courtesy of the researchers To do this, diffusion models learn a procedure for making small improvements to a potential solution. Then, to solve a problem, they start with a random, very bad solution and then gradually improve it.
The models then work together to find solutions, in this case locations for the objects to be placed, that jointly satisfy the constraints. Instead, the researchers reversed the process by coming up with solutions first. They used fast algorithms to generate segmented boxes and fit a diverse set of 3D objects into each segment, ensuring tight packing, stable poses, and collision-free solutions.
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