The system uses millions of previously created object models such as tables, plates and cutlery sets. AI then regulates them in new scenes and prevents the intersection of objects and other errors of 3D graphics, taking into account the physical laws. The method is based on the Monte Carlo Tree search algorithm, which allows the scenes to gradually improve, to reach maximum realism and complexity.
“Resistable Stage Production” allows robots to work in various scenarios using reinforcement training. The system can also respond to user text commands, for example, for some scenes, 98% accuracy can create a kitchen with certain items.
Researchers say that new technology reduces the necessary time and resources to create educational data and provides as close scenes as possible to the real world. In the future, they plan to add the opportunity to create interactive elements such as new objects and products to create interactive elements such as products to make robot training more practical and adaptable.
Source: Ferra
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