Robots manipulate soft, deformable material into various shapes from visual inputs in a new system that could one day enable better home assistants. Many of us feel an overwhelming sense of joy from our inner child when stumbling across a pile of the fluorescent, rubbery mixture of water, salt, a
let robots take their hand at playing with the modeling compound, but not for nostalgia’s sake. Their new system called “RoboCraft” learns directly from visual inputs to let a robot with a two-fingered gripper see, simulate, and shape doughy objects. It could reliably plan a robot’s behavior to pinch and release play dough to make various letters, including ones it had never seen.
When working with undefined, smooth materials, the whole structure must be taken into consideration before any form of efficient and effective modeling and planning can be done. RoboCraft uses a graph neural network as the dynamics model and transforms images into graphs of tiny particles together with algorithms to provide more precise predictions about the material’s change in shape.
Perception — the first part of the system — is all about learning to “see.” It employs cameras to gather raw, visual sensor data from the environment, which are then turned into little clouds of particles to represent the shapes. This particle data is used by a graph-based neural network to learn to “simulate” the object’s dynamics, or how it moves. Armed with the training data from many pinches, algorithms then help plan the robot’s behavior so it learns to “shape” a blob of dough.
A further in the future domain the scientists envision is using RoboCraft for assistance with household tasks and chores, which could be of particular help to the elderly or those with limited mobility. To accomplish this, given the many obstructions that could take place, a much more adaptive representation of the dough or item would be needed, as well as an exploration into what class of models might be suitable to capture the underlying structural systems.
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