MIT researchers have come up with something unique; they created robots that can shape materials and build predictions regarding interacting with solid objects and liquids.
The system, referred to as a learning-based particle machine, may provide industrial robots additional refined bit — and it should make amusing applications in personal robotics, like modeling clay shapes or rolling sticky rice for sushi.
Robots are coached in such a way that they can forecast the outcomes of their interactions with objects, like pushing a solid box or thrust deformable clay.
In a paper being introduced at the International Conference on Learning Representations in May, the researchers describe a model that learns to grab attention however little parts of various materials — “particles” — act once they’re poked and prodded. The model directly learns from data in cases wherever the underlying physics of the movements are unsure. Robots will then use the model as a guide to predict however liquids, further as rigid and deformable materials, can react to the force of bit. Because the automaton handles the objects, the model additionally helps to more refine the robot’s management.
However ancient learning-based simulators principally target rigid objects and are unable to manage fluids or softer objects. Some precise physics-based simulators will handle numerous materials; however, trust heavily on approximation techniques that introduce errors once robots act with objects within the universe.
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